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Use when asked to verify a PR, confirm a fix works, test a change manually, check that a feature works, or validate local changes before pushing.\n- code-review: Review the current diff for correctness bugs and reuse/simplification/efficiency cleanups at the given effort level (low/medium: fewer, high-confidence findings; high→max: broader coverage, may include uncertain findings). Pass --comment to post findings as inline PR comments, or --fix to apply the findings to the working tree after the review.\n- simplify: Review the changed code for reuse, simplification, efficiency, and altitude cleanups, then apply the fixes. Quality only — it does not hunt for bugs; use /code-review for that.\n- fewer-permission-prompts: Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.\n- loop: Run a prompt or slash command on a recurring interval (e.g. /loop 5m /foo). Omit the interval to let the model self-pace. - When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. \"check the deploy every 5 minutes\", \"keep running /babysit-prs\"). Do NOT invoke for one-off tasks.\n- schedule: Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule. - When the user wants to schedule a recurring cloud agent, set up automated tasks, create a cron job for Claude Code, or manage their scheduled agents/routines. 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{"parentUuid":"ab639180-0e66-4766-868c-7e0ba251e92e","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LT95poBAC3Ng66K77Zphik","type":"tool_result","content":"# LAM Repository Root Level Files\n\nBased on the GitHub API response, the root-level contents of the LAM repository include:\n\n**Files:**\n- `.gitignore` – Git ignore configuration\n- `LICENSE` – Primary license file (11.3 KB)\n- `LICENSE_WEIGHT` – Additional license file for weights (19.3 KB)\n- `README.md` – English documentation (7.7 KB)\n- `README_CN.md` – Chinese documentation (7.2 KB)\n- `app_hf_space.py` – Hugging Face Space application (22.3 KB)\n- `app_lam.py` – LAM application file (23 KB)\n- `requirements.txt` – Python dependencies (612 bytes)\n\n**Directories:**\n- `assets/`, `configs/`, `external/`, `lam/`, `scripts/`, `tools/`, `vhap/`\n\nThe repository appears to be a 3D AI generation project with dual licensing (code and model weights handled separately)."}]},"uuid":"51ed9966-30de-4440-9860-b660249c1042","timestamp":"2026-06-15T20:58:37.780Z","sourceToolAssistantUUID":"ab639180-0e66-4766-868c-7e0ba251e92e","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"5db1e28e-d91c-4c5b-b2ce-0bb0a07cb905","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RaNG1vofguZHsUyohLB2iX","type":"tool_result","content":"# Files in the LAM Directory\n\nBased on the repository content, the `lam` directory contains:\n\n**Files:**\n- `__init__.py` (empty Python package file)\n- `launch.py` (1,120 bytes)\n\n**Subdirectories:**\n- `datasets/`\n- `losses/`\n- `models/`\n- `runners/`\n- `utils/`\n\nThe structure appears to be a Python package for an AI/3D project, with modular organization for datasets, loss functions, model definitions, training runners, and utility functions."}]},"uuid":"e6c67a6c-adb3-4c81-a2cf-b374791036f7","timestamp":"2026-06-15T20:58:43.678Z","sourceToolAssistantUUID":"5db1e28e-d91c-4c5b-b2ce-0bb0a07cb905","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"e1f7b952-9278-4808-b33c-b5ee4c31b194","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016PrXECUhKmBeecPGhKy4mi","type":"tool_result","content":"# app_lam.py Download Information\n\n**Download URL:**\n```\nhttps://raw.githubusercontent.com/aigc3d/LAM/master/app_lam.py\n```\n\n**File Details:**\n- **Repository:** aigc3d/LAM\n- **Size:** 23,048 bytes\n- **License:** Apache License 2.0\n- **Encoding:** Base64 (in the API response)\n\n**Content Summary:**\nThis is a Gradio-based web application for the LAM (Large Avatar Model) project. The application enables users to upload front-facing images and generates animated video outputs by:\n\n- Processing input images through flame tracking and segmentation\n- Estimating flame parameters from the input\n- Running inference on a pre-trained LAM model\n- Rendering animated avatars based on motion sequences\n- Exporting results as video files with audio\n\nThe script integrates several computer vision and deep learning tools, including PyTorch, OpenCV, and moviepy for video generation."}]},"uuid":"beb83e3d-0b4d-4a35-bdc1-45c94d9d7342","timestamp":"2026-06-15T20:58:44.433Z","sourceToolAssistantUUID":"e1f7b952-9278-4808-b33c-b5ee4c31b194","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"beb83e3d-0b4d-4a35-bdc1-45c94d9d7342","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01ENTwmxpjZscBKRzwcJLi8q","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01AQSUrhc361sztVnb4U2E3s","name":"WebFetch","input":{"url":"https://raw.githubusercontent.com/aigc3d/LAM/master/app_lam.py","prompt":"Extract the COMPLETE Python code. Specifically: all imports, all class definitions, all function definitions (especially audio-related ones), how Audio2Expression is used, what input formats are accepted for audio, how rendering works, any WebSocket or HTTP server code, the Gradio interface structure, any streaming or real-time processing code"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":158,"cache_creation_input_tokens":528,"cache_read_input_tokens":14654,"cache_creation":{"ephemeral_5m_input_tokens":528,"ephemeral_1h_input_tokens":0},"output_tokens":65,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5iWUGtSKfV3rnVLDpVm","attributionAgent":"general-purpose","type":"assistant","uuid":"adff367d-627e-4aba-a9d2-73955c926ff0","timestamp":"2026-06-15T20:58:48.491Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"adff367d-627e-4aba-a9d2-73955c926ff0","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01ENTwmxpjZscBKRzwcJLi8q","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UGCRgQ72UYs6zG6HPMSUoi","name":"WebFetch","input":{"url":"https://api.github.com/repos/aigc3d/LAM/contents/lam/models/","prompt":"List all files in the lam/models directory"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":158,"cache_creation_input_tokens":528,"cache_read_input_tokens":14654,"output_tokens":247,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":528},"inference_geo":"not_available","iterations":[{"input_tokens":158,"output_tokens":247,"cache_read_input_tokens":14654,"cache_creation_input_tokens":528,"cache_creation":{"ephemeral_5m_input_tokens":528,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5iWUGtSKfV3rnVLDpVm","attributionAgent":"general-purpose","type":"assistant","uuid":"01561fbb-7304-4416-a59f-a12eb93456bd","timestamp":"2026-06-15T20:58:48.912Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"01561fbb-7304-4416-a59f-a12eb93456bd","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UGCRgQ72UYs6zG6HPMSUoi","type":"tool_result","content":"# Files in the lam/models Directory\n\nBased on the repository contents, the lam/models directory contains:\n\n**Files:**\n- `__init__.py` (685 bytes)\n- `block.py` (5,152 bytes)\n- `discriminator.py` (4,479 bytes)\n- `modeling_lam.py` (17,198 bytes)\n- `modulate.py` (1,427 bytes)\n- `transformer.py` (7,685 bytes)\n- `transformer_dit.py` (17,648 bytes)\n\n**Subdirectories:**\n- `encoders/`\n- `rendering/`\n\nThis appears to be part of the LAM (an AI generative 3D model) project, with the main modeling code organized into transformer-based architecture files, discriminator components, and utility modules."}]},"uuid":"1c40ab65-42fa-4aaf-b098-2015a984f65c","timestamp":"2026-06-15T20:58:51.728Z","sourceToolAssistantUUID":"01561fbb-7304-4416-a59f-a12eb93456bd","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"adff367d-627e-4aba-a9d2-73955c926ff0","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01AQSUrhc361sztVnb4U2E3s","type":"tool_result","content":"# Code Analysis: LAM Gradio Application\n\n## Imports & Dependencies\nThe code imports core libraries: `os`, `cv2`, `sys`, `base64`, `subprocess`, `gradio`, `numpy`, `PIL.Image`, `argparse`, `omegaconf`, `torch`, `zipfile`, `glob`, and `moviepy.editor`. Custom modules include flame tracking utilities and LAM model inference components.\n\n## Key Functions\n\n**Audio Processing:**\n```\nadd_audio_to_video(video_path, out_path, audio_path)\n```\nThis function loads a video file and audio file using MoviePy's `VideoFileClip` and `AudioFileClip`, attaches the audio to the video, then exports the combined result as an MP4 using libx264 codec and AAC audio codec.\n\n**Video Creation:**\n```\nsave_images2video(img_lst, v_pth, fps)\n```\nConverts a sequence of images to video using MoviePy's `ImageSequenceClip`, writing output with libx264 codec.\n\n## Audio Input Handling\nAudio is sourced from the file system. The code references: `audio_path = os.path.join(\"./assets/sample_motion/export\", base_vid, base_vid+\".wav\")`. WAV files are expected, loaded from predefined motion directories corresponding to selected video inputs.\n\n## Gradio Interface Structure\nThe application uses `gr.Blocks()` with organized columns for:\n- Input image (upload or examples)\n- Input video (examples from `./assets/sample_motion/export/`)\n- Processed image display\n- Rendered video output\n\nThe interface chains operations: input validation → preprocessing → core inference → video generation with audio overlay.\n\n## Rendering Pipeline\nThe `core_fn()` function orchestrates: FLAME tracking, image preprocessing, motion sequence preparation, and LAM model inference with `torch.no_grad()`. Output includes RGB frames (0-1 normalized) and masks, composited together before video encoding.\n\n**No WebSocket or streaming code exists.** Processing is synchronous with Gradio's queue system."}]},"uuid":"6386899b-fa1c-4fcd-9333-11cdc47660e4","timestamp":"2026-06-15T20:58:56.274Z","sourceToolAssistantUUID":"adff367d-627e-4aba-a9d2-73955c926ff0","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"6386899b-fa1c-4fcd-9333-11cdc47660e4","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01MofeUEXaLeGvfnPnNL8o6Q","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01C9bynaScFhZdZyRufm9M83","name":"WebFetch","input":{"url":"https://api.github.com/repos/aigc3d/LAM/contents/lam/models/rendering/","prompt":"List all files in the rendering subdirectory"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":225,"cache_creation_input_tokens":913,"cache_read_input_tokens":15182,"cache_creation":{"ephemeral_5m_input_tokens":913,"ephemeral_1h_input_tokens":0},"output_tokens":64,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5iXM4LFhwe13x6x4KtC","attributionAgent":"general-purpose","type":"assistant","uuid":"dd15f3ed-ae88-436a-8223-2b88d735b368","timestamp":"2026-06-15T20:58:58.466Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"dd15f3ed-ae88-436a-8223-2b88d735b368","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01MofeUEXaLeGvfnPnNL8o6Q","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NTFn2JmSARUDSCi8BJjSMq","name":"WebFetch","input":{"url":"https://api.github.com/repos/aigc3d/LAM/git/trees/master?recursive=1","prompt":"Find all Python files related to Audio2Expression, audio processing, or chatting/talking. List their full paths."},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":225,"cache_creation_input_tokens":913,"cache_read_input_tokens":15182,"output_tokens":205,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":913},"inference_geo":"not_available","iterations":[{"input_tokens":225,"output_tokens":205,"cache_read_input_tokens":15182,"cache_creation_input_tokens":913,"cache_creation":{"ephemeral_5m_input_tokens":913,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5iXM4LFhwe13x6x4KtC","attributionAgent":"general-purpose","type":"assistant","uuid":"38b32db6-0b23-4952-abac-b5f69f654463","timestamp":"2026-06-15T20:58:59.510Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"dd15f3ed-ae88-436a-8223-2b88d735b368","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01C9bynaScFhZdZyRufm9M83","type":"tool_result","content":"# Files in the Rendering Subdirectory\n\nThe `lam/models/rendering/` directory contains:\n\n**Files:**\n- `__init__.py` (593 bytes)\n- `gaussian_model.py` (7,891 bytes)\n- `gs_renderer.py` (41,015 bytes)\n\n**Subdirectories:**\n- `flame_model/`\n- `utils/`\n\nThe largest file is the Gaussian splatting renderer module at over 41KB, followed by the Gaussian model implementation and initialization files."}]},"uuid":"35279570-4716-4f9a-9d1b-ed7546cec3e8","timestamp":"2026-06-15T20:59:00.951Z","sourceToolAssistantUUID":"dd15f3ed-ae88-436a-8223-2b88d735b368","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"38b32db6-0b23-4952-abac-b5f69f654463","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NTFn2JmSARUDSCi8BJjSMq","type":"tool_result","content":"Based on my analysis of the repository structure provided, I found **no Python files specifically related to Audio2Expression, audio processing, or chatting/talking functionality**.\n\nThe repository (LAM - likely a face/head animation project) contains:\n\n- **Face detection & landmark files**: `external/landmark_detection/` modules\n- **Human matting modules**: `external/human_matting/` \n- **3D rendering & FLAME model**: `lam/models/rendering/` with flame.py files\n- **Video processing**: `lam/datasets/video_head.py`\n- **Inference runners**: `lam/runners/infer/` modules\n- **UI applications**: `app_lam.py`, `app_hf_space.py`\n\nHowever, none of these files appear designed for audio-to-expression conversion, speech recognition, or dialogue systems based on their naming and directory organization. The project focuses on visual face animation and 3D head modeling rather than audio-driven features."}]},"uuid":"a621956c-8c11-4f55-9c60-14e296996f0e","timestamp":"2026-06-15T20:59:04.478Z","sourceToolAssistantUUID":"38b32db6-0b23-4952-abac-b5f69f654463","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"4732dbac-c01b-4ef0-9bfa-0451104153e9","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VyJKThp7sW3RenE9d83Kih","type":"tool_result","content":"# File Paths in LAM Repository\n\nBased on the repository tree provided, here are ALL file paths, with attention to relevant categories:\n\n## Core Application Files\n- `app_hf_space.py`\n- `app_lam.py`\n\n## Configuration Files\n- `configs/inference/lam-20k-8gpu.yaml`\n- `configs/stylematte_config.json`\n- `configs/vhap_tracking/base_tracking_config.yaml`\n\n## Model & Processing Modules\n- `lam/models/modeling_lam.py`\n- `lam/models/transformer.py`\n- `lam/models/transformer_dit.py`\n- `lam/models/discriminator.py`\n\n## Dataset Handling\n- `lam/datasets/video_head.py`\n- `lam/datasets/cam_utils.py`\n\n## Rendering & 3D Components\n- `lam/models/rendering/gs_renderer.py`\n- `lam/models/rendering/gaussian_model.py`\n- `lam/models/rendering/flame_model/flame.py`\n\n## Inference Pipeline\n- `lam/runners/infer/lam.py`\n- `lam/runners/infer/head_utils.py`\n- `lam/runners/infer/utils.py`\n\n## Tracking & Analysis\n- `vhap/model/tracker.py`\n- `vhap/track.py`\n\n## Utility Functions\n- `lam/utils/video.py`\n- `lam/utils/ffmpeg_utils.py`\n\n**Notable absence:** No files explicitly mention audio, expression parameters, chat, talking head functionality, streaming, WebSocket, SDK, or API endpoints in their filenames."}]},"uuid":"c3366ea4-f1a7-443e-93d3-ed3555fb9497","timestamp":"2026-06-15T20:59:15.854Z","sourceToolAssistantUUID":"4732dbac-c01b-4ef0-9bfa-0451104153e9","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"e1f1e2d2-d453-4271-9dc8-161548918e88","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SMm1Sm74ZwWnRrhGsbfALs","type":"tool_result","content":"# LAM: Large Avatar Model - Key Components Summary\n\n## Core Technology\nLAM enables **\"Build 3D Interactive Chatting Avatar with One Image in Seconds!\"** through ultra-realistic 3D avatar creation, cross-platform animation rendering, and low-latency interactive capabilities.\n\n## Audio2Expression Model\nReleased April 19, 2025, this component animates LAM-generated avatars using audio input, enabling dynamic facial expressions driven by sound.\n\n## OpenAvatarChat Integration\nThe WebGL Interactive Chatting Avatar SDK (released April 21, 2025) integrates LAM with OpenAvatarChat, providing a complete ecosystem including language models, automatic speech recognition, text-to-speech, and avatar animation for conversational interactions with generated digital humans.\n\n## Rendering Pipeline\nThe system achieves impressive performance metrics: **\"1.4 s reconstruction time\"** on standard hardware, with **\"562.9FPS\"** animation and rendering on A100 GPUs and over **\"110+FPS\"** on mobile devices like Xiaomi 14 phones.\n\n## WebGL Render\nReleased May 20, 2025, the LAM_WebRender enables cross-platform animation and rendering capabilities, supporting deployment across different devices and environments.\n\n## Export Functionality\nUsers can export LAM-generated avatars to OpenAvatarChat-compatible formats for real-time conversations through the Avatar Export Feature (released April 30, 2025).\n\n## Training Framework\nLAM-20K was trained on VFHQ and NeRSemble datasets, with future plans for expanded models trained on proprietary large-scale datasets."}]},"uuid":"ad180a8e-18f3-48cb-adf1-66b54e21a802","timestamp":"2026-06-15T20:59:18.464Z","sourceToolAssistantUUID":"e1f1e2d2-d453-4271-9dc8-161548918e88","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"011ff4de-e6ee-4129-8348-058a1d66ab4d","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PMEKeR7APS3jgxLHceJEvy","type":"tool_result","content":"# Analysis of Provided Code\n\nBased on the content shared, here's what I found regarding your specific queries:\n\n## Audio Processing\nThe code includes audio handling through this function:\n```python\ndef add_audio_to_video(video_path, out_path, audio_path):\n```\nThis method accepts a WAV file path and attaches it to rendered video output using `moviepy.editor`. The audio source is located at `\"./assets/sample_motion/export/{base_vid}/{base_vid}.wav\"`.\n\n## Expression Generation Approach\nRather than deriving expressions directly from audio, this codebase uses **pre-computed motion sequences**. The system loads FLAME parameters from disk:\n- Motion sequences directory: `\"./assets/sample_motion/export/{base_vid}/flame_param\"`\n- These parameters drive facial animation, independent of audio input\n\n## Gradio Interface Architecture\nThe interface features:\n- **Input tabs**: Image upload and video selection\n- **Processing pipeline**: \"Generate\" button triggers preprocessing → inference → rendering\n- **Output displays**: Processed image and rendered video outputs\n- **Example galleries**: Pre-loaded sample images and motion videos\n\nNo streaming, WebSocket endpoints, or real-time chatting functionality appears in this code.\n\n## Key Finding\nThis is a **one-shot animation system** where audio synchronization occurs through pre-existing motion sequences rather than audio-to-expression conversion. The audio attachment happens post-rendering as a final step."}]},"uuid":"64c83ffe-2059-4b60-9fca-50f7102b51dd","timestamp":"2026-06-15T20:59:29.839Z","sourceToolAssistantUUID":"011ff4de-e6ee-4129-8348-058a1d66ab4d","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"6257dcbf-9c07-4058-9068-e41a19f03a95","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01YGNJmcv78Hg7HktMEeCcWX","type":"tool_result","content":"# LAM Project README Summary\n\n## Core Project Overview\nLAM (Large Avatar Model) creates \"3D Interactive Chatting Avatar with One Image in Seconds!\" It's a PyTorch implementation enabling ultra-realistic avatar generation from single images with fast cross-platform animation.\n\n## Audio2Expression Integration\nReleased April 19, 2025, the Audio2Expression model \"can animate the generated LAM Avatar with audio input.\" This component enables audio-driven facial animation for created avatars.\n\n## OpenAvatarChat Integration\nThe project released an \"Interactive Chatting Avatar SDK\" (April 21, 2025) integrated with OpenAvatarChat. Users can \"freely chat with the 3D Digital Human generated by LAM\" through a system combining \"LLM, ASR, TTS, Avatar\" capabilities. An export feature allows avatars to be converted to formats compatible with OpenAvatarChat conversations.\n\n## WebGL Rendering SDK\nReleased May 20, 2025, the WebGL-Render component enables \"cross-platform animation and rendering\" functionality, supporting deployment on various devices.\n\n## Performance Metrics\n- Reconstruction: 1.4 seconds\n- A100 GPU performance: 562.9 FPS\n- Mobile performance (XiaoMi 14): 110+ FPS\n\n## Key Dependencies\nThe project builds upon OpenLRM, GAGAvatar, GaussianAvatars, and VHAP frameworks."}]},"uuid":"e668d2c9-8092-4d53-b7fc-990a661eeb1f","timestamp":"2026-06-15T20:59:42.714Z","sourceToolAssistantUUID":"6257dcbf-9c07-4058-9068-e41a19f03a95","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"1c4f3a1e-f08d-4db1-99f0-84b42a8ff075","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_019EADofzjqvuzE7gvTqrF3a","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011rHN8mtvGikbMp7vprRjXS","name":"WebFetch","input":{"url":"https://raw.githubusercontent.com/aigc3d/OpenAvatarChat/refs/heads/main/README.md","prompt":"Full README content"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":635,"cache_read_input_tokens":19147,"output_tokens":190,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":635},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":190,"cache_read_input_tokens":19147,"cache_creation_input_tokens":635,"cache_creation":{"ephemeral_5m_input_tokens":635,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5iama4RNzroAPU3Bhws","attributionAgent":"general-purpose","type":"assistant","uuid":"7a0aa349-53cb-46d6-801b-54c5fe9ac7aa","timestamp":"2026-06-15T20:59:47.539Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"7a0aa349-53cb-46d6-801b-54c5fe9ac7aa","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011rHN8mtvGikbMp7vprRjXS","type":"tool_result","content":"The server returned HTTP 404 Not Found.\n\nThe response body was not retrieved. If this URL requires authentication, use an authenticated tool (e.g. `gh` for GitHub, or an MCP-provided fetch tool) instead of WebFetch."}]},"uuid":"f4e6a123-a59e-4e5c-9a5a-ba337545f9cf","timestamp":"2026-06-15T20:59:47.682Z","sourceToolAssistantUUID":"7a0aa349-53cb-46d6-801b-54c5fe9ac7aa","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"f4e6a123-a59e-4e5c-9a5a-ba337545f9cf","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01Ug7aUnDc5z2iacgMD7BDvp","type":"message","role":"assistant","content":[{"type":"text","text":"The OpenAvatarChat repo isn't responding to direct raw fetches. Let me try alternative approaches."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":458,"cache_read_input_tokens":19782,"cache_creation":{"ephemeral_5m_input_tokens":458,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5ib8mmWurhSBv8MDT1V","attributionAgent":"general-purpose","type":"assistant","uuid":"d2971281-341d-4ff6-ba84-c76c7f9b08c1","timestamp":"2026-06-15T20:59:51.405Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"2fb50b0e-48c9-4539-a214-a60c853e9b76","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01Ug7aUnDc5z2iacgMD7BDvp","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MDD1qzKQrAqJstkVKAztjB","name":"WebFetch","input":{"url":"https://raw.githubusercontent.com/aigc3d/LAM/master/lam/runners/infer/lam.py","prompt":"Extract the complete Python code: class definitions, function signatures, how inference works, input/output formats, especially anything related to audio or expression parameters"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":458,"cache_read_input_tokens":19782,"cache_creation":{"ephemeral_5m_input_tokens":458,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5ib8mmWurhSBv8MDT1V","attributionAgent":"general-purpose","type":"assistant","uuid":"8f2cdf7f-e07b-46d3-bcd3-d607ea41be49","timestamp":"2026-06-15T20:59:53.280Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"8f2cdf7f-e07b-46d3-bcd3-d607ea41be49","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MDD1qzKQrAqJstkVKAztjB","type":"tool_result","content":"# LAM Inferrer: Code Structure and Inference Pipeline\n\n## Class Definition\n\n```python\n@REGISTRY_RUNNERS.register('infer.lam')\nclass LAMInferrer(Inferrer):\n    EXP_TYPE: str = 'lam'\n    \n    def __init__(self):\n        self.cfg = parse_configs()\n        self.model: LAMInferrer = self._build_model(self.cfg).to(self.device)\n        self.flametracking = FlameTrackingSingleImage(...)\n```\n\n## Core Methods\n\n**Model Building:**\n```python\ndef _build_model(self, cfg):\n    model = ModelLAM(**cfg.model)\n    ckpt = load_file(resume, device='cpu')\n    # Loads state_dict with shape validation\n```\n\n**Inference Pipeline:**\n```python\ndef infer_single(self, image_path, motion_seqs_dir, motion_img_dir, \n                 motion_video_read_fps, export_video, export_mesh, \n                 dump_tmp_dir, dump_image_dir, dump_video_path, \n                 dump_mesh_path, gaga_track_type)\n```\n\n## Input/Output Formats\n\n**Inputs:**\n- Single image path (preprocessed to `source_size`)\n- Motion sequences directory with video data\n- FLAME tracking parameters (shape parameters, facial keypoints)\n- Camera parameters (extrinsics, intrinsics)\n\n**Outputs:**\n- RGB frames: `[Nv, H, W, 3]` (0-1 range, converted to uint8)\n- Video with audio: MP4 format at specified `render_fps`\n- Mesh exports: PLY and OBJ formats\n- Gaussian splatting representations\n\n## Audio Handling\n\n```python\ndef add_audio_to_video(self, video_path, out_path, audio_path):\n    video_clip = VideoFileClip(video_path)\n    audio_clip = AudioFileClip(audio_path)\n    video_clip_with_audio = video_clip.set_audio(audio_clip)\n    video_clip_with_audio.write_videofile(out_path, codec='libx264')\n```\n\nAudio source: `base_vid.wav` located in `motion_seqs_dir`\n\n## Expression Parameters\n\n**FLAME Parameters Structure:**\n```python\nmotion_seq[\"flame_params\"] = {\n    \"betas\": shape_param.unsqueeze(0),  # Shape coefficients\n    # Additional FLAME parameters from motion sequences\n}\n```\n\nModel processes: `flame_params={k:v.to(device) for k, v in motion_seq[\"flame_params\"].items()}`\n\n## Inference Execution\n\n```python\nwith torch.no_grad():\n    res = self.model.infer_single_view(\n        image.unsqueeze(0).to(device, dtype),\n        None, None,\n        render_c2ws=motion_seq[\"render_c2ws\"],\n        render_intrs=motion_seq[\"render_intrs\"],\n        render_bg_colors=motion_seq[\"render_bg_colors\"],\n        flame_params={...}\n    )\n```\n\n**Returns:** Dictionary containing `comp_rgb`, `cano_gs_lst`, `3dgs` representations"}]},"uuid":"e6e81388-1b8a-4a7a-a3d0-0d5a538e988c","timestamp":"2026-06-15T21:00:01.436Z","sourceToolAssistantUUID":"8f2cdf7f-e07b-46d3-bcd3-d607ea41be49","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"e6e81388-1b8a-4a7a-a3d0-0d5a538e988c","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_017Hqh7V2pjaDdTLvDHJhPCg","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013ZN8tNxSZaDpMyj3JGWQHG","name":"WebFetch","input":{"url":"https://raw.githubusercontent.com/aigc3d/LAM/master/lam/models/modeling_lam.py","prompt":"Extract class definitions and key method signatures, especially: how FLAME parameters drive animation, what the model's infer_single_view method accepts, what expression/pose parameters look like, any audio-related code"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1140,"cache_read_input_tokens":20240,"cache_creation":{"ephemeral_5m_input_tokens":1140,"ephemeral_1h_input_tokens":0},"output_tokens":55,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5ic9bA3QFGtoX29YmM3","attributionAgent":"general-purpose","type":"assistant","uuid":"23e390ab-4944-4260-9e6b-682d8d3ec373","timestamp":"2026-06-15T21:00:04.948Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"23e390ab-4944-4260-9e6b-682d8d3ec373","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_017Hqh7V2pjaDdTLvDHJhPCg","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011UDg3DYwUN1rRfPj29kvhD","name":"WebFetch","input":{"url":"https://github.com/aigc3d/LAM/issues?q=audio+expression","prompt":"List any issues mentioning Audio2Expression, real-time, streaming, WebSocket, or API integration"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1140,"cache_read_input_tokens":20240,"output_tokens":233,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1140},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":233,"cache_read_input_tokens":20240,"cache_creation_input_tokens":1140,"cache_creation":{"ephemeral_5m_input_tokens":1140,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5ic9bA3QFGtoX29YmM3","attributionAgent":"general-purpose","type":"assistant","uuid":"00bef9eb-c40f-4ef2-a780-aa469b8aa5f4","timestamp":"2026-06-15T21:00:06.010Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"23e390ab-4944-4260-9e6b-682d8d3ec373","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013ZN8tNxSZaDpMyj3JGWQHG","type":"tool_result","content":"# LAM Model Analysis\n\n## Class Definition\n\n**ModelLAM** is a PyTorch module for single-view 3D face reconstruction using Gaussian Splatting and FLAME parametric models.\n\n## Key Method Signatures\n\n### `infer_single_view()`\nAccepts these parameters:\n- `image`: [B, N_ref, C_img, H_img, W_img] - reference images\n- `source_c2ws`, `source_intrs`: camera poses and intrinsics\n- `render_c2ws`, `render_intrs`: target view camera data\n- `render_bg_colors`: [B, N_source, 3]\n- `flame_params`: Dictionary containing pose/expression parameters\n\nReturns rendered outputs including RGB, depth, masks, and canonical GS models.\n\n## FLAME Parameter Structure\n\nThe model expects `flame_params` as a dictionary with:\n- `\"betas\"`: [B, 100] - shape parameters\n- `\"expr\"`: [B, 50] - expression parameters  \n- `\"pose_shape\"`: [B, N_source, 21, 3] - pose data\n\nThese parameters drive animation through the renderer's forward pass, which generates posed/expressed geometry.\n\n## Animation Pipeline\n\nThe renderer processes flame parameters to:\n1. Generate query points from parametric geometry\n2. Deform mesh based on shape/expression/pose\n3. Render multiple views with Gaussian Splatting\n\n## Audio-Related Code\n\n**None detected** in the provided source. This codebase focuses on visual reconstruction without audio-driven animation components.\n\n## Query Points Generation\n\nFor `e2e_flame` mode, query points are dynamically computed via `renderer.get_query_points(flame_params, device=image.device)`, enabling end-to-end optimization without fixed embeddings."}]},"uuid":"d027d249-fba5-4b6f-bd4d-7005437092f9","timestamp":"2026-06-15T21:00:10.413Z","sourceToolAssistantUUID":"23e390ab-4944-4260-9e6b-682d8d3ec373","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"00bef9eb-c40f-4ef2-a780-aa469b8aa5f4","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011UDg3DYwUN1rRfPj29kvhD","type":"tool_result","content":"Based on the web page content provided, I can identify only **one issue** related to your search criteria:\n\n**Issue #91: \"Is LAM_audio2exp.tar publicly available or planned for release?\"**\n- **Status:** Open\n- **Author:** mirai-gpro\n- **Date:** February 25, 2026\n- **Repository:** aigc3d/LAM\n\nThis issue directly addresses the Audio2Expression component. However, the page content does not display the full issue description, so I cannot confirm whether it mentions real-time capabilities, streaming, WebSocket, or API integration specifics.\n\nThe search results shown are limited, displaying only this single matching issue from the repository's 55 total open issues."}]},"uuid":"d7bf5975-21dd-4125-b33f-861de8f4fe01","timestamp":"2026-06-15T21:00:13.119Z","sourceToolAssistantUUID":"00bef9eb-c40f-4ef2-a780-aa469b8aa5f4","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"8d0c5d4e-719d-4009-bc81-32597844d333","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01KqCSpgrbbZREE3EHeBcEHk","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DqYJLt8udinpifba4m2uuw","name":"WebFetch","input":{"url":"https://github.com/aigc3d/LAM/issues?q=audio2expression+OR+audio2exp+OR+OpenAvatarChat&state=all","prompt":"List all issues related to Audio2Expression, OpenAvatarChat integration, real-time rendering, or API usage"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":869,"cache_read_input_tokens":21380,"output_tokens":224,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":869},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":224,"cache_read_input_tokens":21380,"cache_creation_input_tokens":869,"cache_creation":{"ephemeral_5m_input_tokens":869,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5id1ZEomXJyPQS7fqKx","attributionAgent":"general-purpose","type":"assistant","uuid":"44ede26f-a7c8-4d0d-9825-1457884760c6","timestamp":"2026-06-15T21:00:17.855Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"8d0c5d4e-719d-4009-bc81-32597844d333","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012tQniQ54PWyiBihPTN9wrG","type":"tool_result","content":"# LAM Audio2Expression Issue Summary\n\n## Issue Overview\nA user inquired about the availability of **LAM_audio2exp.tar**, a model file required for the audio-to-expression pipeline in the LAM project.\n\n## Key Details\n\n**Issue #91** was opened on February 25, 2026 by user @mirai-gpro in the aigc3d/LAM repository.\n\n**User's Question:**\nThe contributor stated: \"the model file LAM_audio2exp.tar does not seem to be publicly available for download\" and asked whether it's hosted elsewhere or has planned release dates.\n\n## What's Involved\n- **File**: LAM_audio2exp.tar (a compressed model archive)\n- **Purpose**: Audio-to-expression conversion pipeline component\n- **Status**: Unclear availability at time of issue opening\n\n## Note\nThe provided content shows only the issue description itself. **No comments or responses from maintainers are included** in the material shared, so there's no resolution or additional context about the actual availability status of this model file.\n\nTo get current information, you would need to check the full issue thread on GitHub or contact the aigc3d/LAM project maintainers directly."}]},"uuid":"07fa2cbe-61d5-4c6c-a690-b41a90407a93","timestamp":"2026-06-15T21:00:20.960Z","sourceToolAssistantUUID":"8d0c5d4e-719d-4009-bc81-32597844d333","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"44ede26f-a7c8-4d0d-9825-1457884760c6","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DqYJLt8udinpifba4m2uuw","type":"tool_result","content":"# Issues Related to Audio2Expression and OpenAvatarChat\n\nBased on the GitHub search results for the LAM repository, here are the relevant issues:\n\n## Open Issues\n\n1. **Is LAM_audio2exp.tar publicly available?** (#91)\n   - Opened Feb 25, 2026 by mirai-gpro\n   - Addresses availability of the audio expression model\n\n2. **Cloud Run deployment fixes** (#82)\n   - Open pull request focused on \"bypass DDP init, use /tmp for writes\"\n   - Addresses infrastructure and deployment concerns\n\n3. **Android demo guide request** (#66)\n   - Opened Oct 29, 2025\n   - Asks whether documentation exists \"with LAM\" for mobile implementation\n\n4. **Custom avatar creation** (#58)\n   - Opened Sep 12, 2025\n   - Feature request for user-generated content\n\n5. **Interactive Chatting Demo deployment** (#32)\n   - Requests guidance on \"deploying Interactive Chatting Demo\"\n   - Relates to OpenAvatarChat integration\n\n## Completed Work\n\n- **Export feature for OpenAvatarChat** (#25) - Merged April 30, 2025\n\nThe search highlights ongoing development around audio-to-expression generation, avatar chat functionality, and deployment challenges rather than API-specific issues."}]},"uuid":"4a173b23-3a03-4152-91d9-815b861f084b","timestamp":"2026-06-15T21:00:22.726Z","sourceToolAssistantUUID":"44ede26f-a7c8-4d0d-9825-1457884760c6","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"a633b6c3-169a-4e59-b00f-7eeb603f48cb","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01D3v1D5TeKUfS27nEpzaDrs","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01S5UjpXUfncqJt97hqwJWx7","name":"WebFetch","input":{"url":"https://github.com/aigc3d/OpenAvatarChat/blob/main/src/engine/engine.py","prompt":"Extract all Python code, class definitions, function signatures, especially audio handling, rendering pipeline, WebSocket/WebRTC code"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":851,"cache_read_input_tokens":22249,"cache_creation":{"ephemeral_5m_input_tokens":851,"ephemeral_1h_input_tokens":0},"output_tokens":55,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5idieH9b9yWXSBr2f2V","attributionAgent":"general-purpose","type":"assistant","uuid":"197c88b2-5786-45cf-8030-712fbdb0f336","timestamp":"2026-06-15T21:00:26.610Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"197c88b2-5786-45cf-8030-712fbdb0f336","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01S5UjpXUfncqJt97hqwJWx7","type":"tool_result","content":"The server returned HTTP 404 Not Found.\n\nThe response body was not retrieved. If this URL requires authentication, use an authenticated tool (e.g. `gh` for GitHub, or an MCP-provided fetch tool) instead of WebFetch."}]},"uuid":"96fde739-a877-4cb0-b8cc-fc54a409af67","timestamp":"2026-06-15T21:00:26.900Z","sourceToolAssistantUUID":"197c88b2-5786-45cf-8030-712fbdb0f336","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"a633b6c3-169a-4e59-b00f-7eeb603f48cb","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GYUAiPqZqxUwZWe2UyvVyY","type":"tool_result","content":"# Issue #32: Interactive Chatting Demo Deployment\n\nBased on the provided GitHub issue page, there is **no substantive content to extract**. \n\nThe issue titled \"如何部署Interactive Chatting Demo？\" (How to deploy Interactive Chatting Demo?) was opened by user KZF-kzf on May 21, 2025, but the issue description states: **\"No description provided.\"**\n\nThe page shows no comments, responses, or additional context regarding deployment instructions or OpenAvatarChat integration details. The issue appears to be an unanswered question without any discussion or resolution documented on this page.\n\nTo find deployment information, you would need to:\n- Check the repository's README or documentation files\n- Review other closed issues or pull requests\n- Contact the repository maintainers directly"}]},"uuid":"41a8b30d-7083-4d1c-a49a-fd27ef71c408","timestamp":"2026-06-15T21:00:28.890Z","sourceToolAssistantUUID":"a633b6c3-169a-4e59-b00f-7eeb603f48cb","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"c020e44d-e074-45c1-a299-d144f01aaf45","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01W5VWu4PZq8Z1fjvJSLkmPD","type":"tool_result","content":"# LAM: Large Avatar Model - README Summary\n\n## Project Overview\nLAM is a PyTorch implementation for creating realistic 3D avatars from single images. The project enables \"building interactive chatting avatars with one image in seconds.\"\n\n## Key Features\nThe system offers three main capabilities:\n- Ultra-realistic 3D avatar generation from a single image\n- Fast cross-platform animation and rendering\n- Low-latency SDK for real-time interactive conversations\n\n## Audio2Expression Model\nReleased on April 19, 2025, this component \"can animate the generated LAM Avatar with audio input.\" It enables audio-driven facial animation for generated avatars.\n\n## OpenAvatarChat Integration\nThe project provides an interactive chatting SDK (released April 21, 2025) that integrates with OpenAvatarChat. Users can \"freely chat with the 3D Digital Human generated by LAM\" using a complete stack including language models, speech recognition, text-to-speech, and avatar rendering.\n\n## Avatar Export Feature\nReleased April 30, 2025, this capability \"allows users to chat with any LAM-generated 3D digital humans on OpenAvatarChat.\" Export guidance is available in the documentation.\n\n## Technical Pipeline\n**Setup & Inference Flow:**\n1. Environment installation (Linux/Windows with CUDA 12.1 or 11.8)\n2. Download model weights and assets from HuggingFace or ModelScope\n3. Run Gradio interface: `python app_lam.py`\n4. Execute inference via: `sh ./scripts/inference.sh ${CONFIG} ${MODEL_NAME} ${IMAGE_PATH} ${MOTION_SEQ}`\n\n## Model Specifications\nLAM-20K achieves 1.4-second reconstruction time on A100 GPUs and 110+ FPS on mobile devices (XiaoMi 14 Phone)."}]},"uuid":"d82a5ddb-8073-47f3-a4c1-dc94c1fbad88","timestamp":"2026-06-15T21:01:09.137Z","sourceToolAssistantUUID":"c020e44d-e074-45c1-a299-d144f01aaf45","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"0550cfeb-ed67-4faf-8228-e89b0da4fa02","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01B66fpzQSc7F2RCGiSxGqui","type":"tool_result","content":"Based on the web page content provided, there are **no visible files or code content** displayed in this search result.\n\nThe page shows a GitHub code search interface for the repository `repo:aigc3d/OpenAvatarChat`, but the actual search results are not loaded. Instead, the page displays a message stating: \"Sign in to search code on GitHub—Before you can access our code search functionality please sign in or create a free account.\"\n\nTo view the code files and content from this repository, a user would need to authenticate by signing in or creating a GitHub account first."}]},"uuid":"22cf232d-81a6-4f24-9b71-5e481cd8eaee","timestamp":"2026-06-15T21:01:09.456Z","sourceToolAssistantUUID":"0550cfeb-ed67-4faf-8228-e89b0da4fa02","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"22cf232d-81a6-4f24-9b71-5e481cd8eaee","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_012yonyAvHhveU7btzSHn9mC","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01M5CcZXa8Jht7RfzZPDwddd","name":"WebFetch","input":{"url":"https://modelscope.cn/models/iic/LAM-20K","prompt":"Extract any documentation about the Audio2Expression pipeline, input/output formats, API details, and integration instructions"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":449,"cache_creation_input_tokens":462,"cache_read_input_tokens":25983,"cache_creation":{"ephemeral_5m_input_tokens":462,"ephemeral_1h_input_tokens":0},"output_tokens":42,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5ihARQVXFKEpHvbPLWH","attributionAgent":"general-purpose","type":"assistant","uuid":"e9c97d78-2781-4cf7-b6e7-baa9cc4a07f7","timestamp":"2026-06-15T21:01:13.745Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"e9c97d78-2781-4cf7-b6e7-baa9cc4a07f7","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_012yonyAvHhveU7btzSHn9mC","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NhLNFDTqG2JEGvSAVLfXQL","name":"WebFetch","input":{"url":"https://github.com/aigc3d/LAM/blob/master/scripts/inference.sh","prompt":"Extract the complete shell script content"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":449,"cache_creation_input_tokens":462,"cache_read_input_tokens":25983,"output_tokens":187,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":462},"inference_geo":"not_available","iterations":[{"input_tokens":449,"output_tokens":187,"cache_read_input_tokens":25983,"cache_creation_input_tokens":462,"cache_creation":{"ephemeral_5m_input_tokens":462,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5ihARQVXFKEpHvbPLWH","attributionAgent":"general-purpose","type":"assistant","uuid":"dbb6a610-aa5a-473b-8b00-d9a700ba4031","timestamp":"2026-06-15T21:01:14.057Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"dbb6a610-aa5a-473b-8b00-d9a700ba4031","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NhLNFDTqG2JEGvSAVLfXQL","type":"tool_result","content":"# Complete Shell Script Content from inference.sh\n\nHere is the full script extracted from the GitHub repository:\n\n```bash\n# step1. set TRAIN_CONFIG path to config file\n\nTRAIN_CONFIG=\"configs/inference/lam-20k-8gpu.yaml\"\n\nMODEL_NAME=\"model_zoo/lam_models/releases/lam/lam-20k/step_045500/\"\n\nIMAGE_INPUT=\"assets/sample_input/status.png\"\n\nMOTION_SEQS_DIR=\"assets/sample_motion/export/Look_In_My_Eyes/\"\n\nTRAIN_CONFIG=${1:-$TRAIN_CONFIG}\n\nMODEL_NAME=${2:-$MODEL_NAME}\n\nIMAGE_INPUT=${3:-$IMAGE_INPUT}\n\nMOTION_SEQS_DIR=${4:-$MOTION_SEQS_DIR}\n\necho \"TRAIN_CONFIG: $TRAIN_CONFIG\"\n\necho \"IMAGE_INPUT: $IMAGE_INPUT\"\n\necho \"MODEL_NAME: $MODEL_NAME\"\n\necho \"MOTION_SEQS_DIR: $MOTION_SEQS_DIR\"\n\nMOTION_IMG_DIR=null\n\nSAVE_PLY=false\n\nSAVE_IMG=false\n\nVIS_MOTION=false\n\nMOTION_IMG_NEED_MASK=true\n\nRENDER_FPS=30\n\nMOTION_VIDEO_READ_FPS=30\n\nEXPORT_VIDEO=true\n\nCROSS_ID=false\n\nTEST_SAMPLE=false\n\nGAGA_TRACK_TYPE=\"\"\n\ndevice=0\n\nnodes=0\n\nexport PYTHONPATH=$PYTHONPATH:$pwd\n\nCUDA_VISIBLE_DEVICES=$device python -m lam.launch infer.lam --config $TRAIN_CONFIG \\\n\nmodel_name=$MODEL_NAME image_input=$IMAGE_INPUT \\\n\nexport_video=$EXPORT_VIDEO export_mesh=$EXPORT_MESH \\\n\nmotion_seqs_dir=$MOTION_SEQS_DIR motion_img_dir=$MOTION_IMG_DIR \\\n\nvis_motion=$VIS_MOTION motion_img_need_mask=$MOTION_IMG_NEED_MASK \\\n\nrender_fps=$RENDER_FPS motion_video_read_fps=$MOTION_VIDEO_READ_FPS \\\n\nsave_ply=$SAVE_PLY save_img=$SAVE_IMG \\\n\ngaga_track_type=$GAGA_TRACK_TYPE cross_id=$CROSS_ID \\\n\ntest_sample=$TEST_SAMPLE rank=$device nodes=$nodes\n```\n\nThe script initializes configuration parameters with defaults, accepts command-line arguments to override them, and executes a Python inference module with specified model and data paths."}]},"uuid":"ddb90bb4-cf69-4778-a778-c330d11f2896","timestamp":"2026-06-15T21:01:18.460Z","sourceToolAssistantUUID":"dbb6a610-aa5a-473b-8b00-d9a700ba4031","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"e9c97d78-2781-4cf7-b6e7-baa9cc4a07f7","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01M5CcZXa8Jht7RfzZPDwddd","type":"tool_result","content":"# Audio2Expression Pipeline Documentation\n\nI cannot provide the requested documentation about the Audio2Expression pipeline, as the webpage content provided contains only a header title (\"ModelScope 魔搭社区\" - ModelScope Magic Community) with no substantive documentation, API details, input/output specifications, or integration instructions.\n\nTo extract this information, I would need:\n- The full webpage content or documentation pages\n- Links to relevant technical guides\n- API reference materials\n- Integration examples or tutorials\n\n**Recommendation:** Please provide the complete documentation pages or direct links to the Audio2Expression pipeline documentation on the ModelScope platform."}]},"uuid":"e3fff707-b691-42fc-8f7c-cb0a0b992260","timestamp":"2026-06-15T21:01:45.933Z","sourceToolAssistantUUID":"e9c97d78-2781-4cf7-b6e7-baa9cc4a07f7","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"fe17fcf1-3464-44e9-b204-d2462ab80796","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01CWjEB14zwcxyAWCZacisX5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CnJejZDbwdZqpP8VXpcZud","name":"WebFetch","input":{"url":"https://huggingface.co/spaces/3DAIGC/LAM-Interactive","prompt":"Extract any visible documentation, code, or pipeline details about Audio2Expression, real-time chat, WebRTC/WebSocket rendering, and how audio drives avatar animation"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":645,"cache_creation_input_tokens":794,"cache_read_input_tokens":26445,"cache_creation":{"ephemeral_5m_input_tokens":794,"ephemeral_1h_input_tokens":0},"output_tokens":54,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5ijrUEyaBewBGv8TS9Y","attributionAgent":"general-purpose","type":"assistant","uuid":"3f18a340-a3fd-4512-a0e0-2354006644fb","timestamp":"2026-06-15T21:01:51.480Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"fe17fcf1-3464-44e9-b204-d2462ab80796","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FdkStawq7nQQ83bq3SBtMe","type":"tool_result","content":"# Python Code Analysis: FLAME Parameters and Motion Sequences\n\n## FLAME Parameters Structure\n\nThe code demonstrates FLAME (Fully Learned Articulated Model of Expression) parameters as PyTorch tensors:\n\n```python\nflame_param_tensor = {\n    'expr': torch.FloatTensor,        # Expression coefficients\n    'rotation': torch.FloatTensor,    # Global head rotation\n    'neck_pose': torch.FloatTensor,   # Neck joint angles\n    'jaw_pose': torch.FloatTensor,    # Jaw articulation\n    'eyes_pose': torch.FloatTensor,   # Eye gaze direction\n    'translation': torch.FloatTensor, # 3D position\n    'betas': torch.FloatTensor,       # Shape parameters\n    'teeth_bs': torch.FloatTensor     # Optional teeth blendshapes\n}\n```\n\n## Motion Sequence Format\n\nMotion sequences are loaded from structured directories containing:\n- `.npz` files storing individual frame FLAME parameters\n- `transforms.json` metadata referencing camera poses and flame paths\n- Optional `tracked_teeth_bs.npz` for dental animation\n- `canonical_flame_param.npz` storing identity-specific shape data\n\n## Audio-Related Code\n\n**No audio processing is present** in this codebase. The file handles visual data (images, video frames) and parametric face model data exclusively.\n\n## Key Processing Pipeline\n\nImage preprocessing includes: padding, masking, center-cropping per facial regions, aspect ratio maintenance, and resizing for neural network input compatibility."}]},"uuid":"9eb9086d-b5c0-48ac-99ad-57555ba1a31c","timestamp":"2026-06-15T21:01:55.481Z","sourceToolAssistantUUID":"fe17fcf1-3464-44e9-b204-d2462ab80796","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"a29c77d5-86d8-469a-9c90-b0d4a1523ce6","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01X8UPi8rrD2g1Po5oQptEfM","type":"tool_result","content":"# LAM GitHub Issues - Audio-Related Search Results\n\nBased on the GitHub issues page for the aigc3d/LAM repository, there is **one audio-related issue** visible:\n\n## Issue #91: LAM_audio2exp.tar Availability\n\n**Status:** Open  \n**Opened:** February 25, 2026  \n**Author:** mirai-gpro\n\n**Summary:** The issue asks whether \"LAM\\_audio2exp.tar is publicly available or planned for release?\"\n\nThis appears to be a inquiry about accessing a specific audio-to-expression model file. The page shows this is the only audio-related issue currently appearing in search results for the repository, which has 55 total issues and 1,000 stars.\n\nThe repository itself (aigc3d/LAM) focuses on AI-generated content for 3D applications, which may explain the interest in audio-to-expression conversion technology."}]},"uuid":"58f70907-53ac-4a7a-ae5f-1beb4a7a3620","timestamp":"2026-06-15T21:02:07.467Z","sourceToolAssistantUUID":"a29c77d5-86d8-469a-9c90-b0d4a1523ce6","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"c5376a90-8901-464d-b767-012817b8d53d","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RsvHgcSkTMLH3TVRoHcEzy","type":"tool_result","content":"# LAM Audio2Expression Model Availability\n\nBased on the GitHub issue, a user named mirai-gpro inquired about the availability of the `LAM_audio2exp.tar` model file on February 25, 2026.\n\n## The Question\n\nThe user asked: \"Is LAM_audio2exp.tar currently hosted somewhere that I might have missed? If it has not been released yet, are there any plans to make it publicly available?\"\n\n## Current Status\n\n**No maintainer response is shown in the provided content.** The issue remains open without any replies from the project maintainers addressing whether:\n- The model file exists on a hosting platform\n- A public release is planned\n- Alternative resources are available\n\nTo get an answer, you would need to check the full issue thread on the [LAM GitHub repository](https://github.com/aigc3d/LAM/issues/91) for any subsequent comments from the project team."}]},"uuid":"f1ef4f1c-508e-4368-8c60-bd7b023f0c05","timestamp":"2026-06-15T21:02:14.006Z","sourceToolAssistantUUID":"c5376a90-8901-464d-b767-012817b8d53d","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"304cc967-ab2e-4b13-ba05-79ab17d7b8bc","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NQF3tYus4bwggxVBaK2stk","type":"tool_result","content":"# Gaussian Splatting Renderer: Key Classes and Methods\n\n## Primary Class: `GS3DRenderer`\n\n**Constructor Parameters:**\n```python\ndef __init__(self, human_model_path, subdivide_num, smpl_type, feat_dim, query_dim,\n             use_rgb, sh_degree, xyz_offset_max_step, mlp_network_config,\n             expr_param_dim, shape_param_dim, ...)\n```\n\nInitializes a FLAME-based head model with configurable Gaussian splatting parameters.\n\n## Main Forward Pass\n\n**Signature:**\n```python\ndef forward(self, gs_hidden_features: Float[Tensor, \"B Np Cp\"],\n            query_points: Float[Tensor, \"B Np 3\"],\n            flame_data, c2w: Float[Tensor, \"B Nv 4 4\"],\n            intrinsic: Float[Tensor, \"B Nv 4 4\"],\n            height, width, additional_features=None,\n            background_color=None, debug=False, **kwargs)\n```\n\n**Flow:**\n1. Processes hidden features through `forward_gs()` to generate Gaussian model\n2. Animates Gaussians using FLAME parameters via `animate_gs_model()`\n3. Renders from multiple camera viewpoints\n\n## FLAME Parameter Consumption\n\nThe renderer expects flame_data dictionary with these keys:\n- `\"expr\"` - expression parameters [Nv, 100]\n- `\"betas\"` - shape parameters [B, 100]\n- `\"rotation\"`, `\"neck_pose\"`, `\"jaw_pose\"`, `\"eyes_pose\"` - pose parameters\n- `\"translation\"` - translation vector\n- `\"teeth_bs\"` (optional) - teeth blend shapes\n\n## Key Output Components\n\n```python\nout = {\"comp_rgb\": [B, V, C, H, W],\n       \"comp_mask\": [B, V, C, H, W],\n       \"comp_depth\": [B, V, C, H, W],\n       \"3dgs\": list[GaussianModel]}\n```\n\nThe renderer returns rendered RGB, silhouette masks, depth maps, and 3D Gaussian splatting objects per batch and viewpoint."}]},"uuid":"03759cde-7695-4493-b41f-734352a26daf","timestamp":"2026-06-15T21:02:36.103Z","sourceToolAssistantUUID":"304cc967-ab2e-4b13-ba05-79ab17d7b8bc","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"8019f8f4-21ae-47db-a039-fffbe4dda83f","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_015Pm2dhMfLLuigmSudwuUtY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LFK8yp4fkKdaF2UCsNUoxb","name":"WebFetch","input":{"url":"https://github.com/aigc3d/LAM/blob/master/app_lam.py","prompt":"Extract the complete code of app_lam.py, all function definitions, class definitions, audio2expression usage, gradio interface structure, any import of audio2expression or chatting modules"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":892,"cache_read_input_tokens":30413,"cache_creation":{"ephemeral_5m_input_tokens":892,"ephemeral_1h_input_tokens":0},"output_tokens":61,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5ioYwQC3x75uaoQCRsa","attributionAgent":"general-purpose","type":"assistant","uuid":"61f8f524-08e5-4d8c-84fd-bbd224e9137a","timestamp":"2026-06-15T21:02:43.261Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"8019f8f4-21ae-47db-a039-fffbe4dda83f","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HKoW9VuxmcqDwxzxDGxKDk","type":"tool_result","content":"# LAM Launch Entry Point Analysis\n\n## Code Overview\n\nThe `launch.py` file serves as the main entry point for the LAM (Alibaba 3DAIGC) project. Here's the complete mechanism:\n\n## Core Functionality\n\n**Argument Parsing**: The script uses `argparse` to accept a runner name as a command-line argument. The parser expects users to specify which runner to execute.\n\n**Runner Registration**: The code references `REGISTRY_RUNNERS`, a dictionary that maps runner names to their corresponding classes. This implements a factory pattern for runner instantiation.\n\n**Execution Flow**: \n1. Parse command-line arguments to identify the requested runner\n2. Validate that the runner exists in the registry\n3. Instantiate the runner class using a context manager\n4. Execute the runner's `run()` method\n\n## Key Design Pattern\n\nThe implementation uses a context manager (`with` statement) to handle runner lifecycle, ensuring proper resource initialization and cleanup. If a requested runner isn't found, the code raises a `ValueError` with a descriptive message.\n\nThis design promotes modularity by decoupling the launch mechanism from specific runner implementations, allowing new runners to be registered without modifying the launch code itself."}]},"uuid":"8fda7fc0-a805-4f8e-aa3e-85f581f57be9","timestamp":"2026-06-15T21:02:46.552Z","sourceToolAssistantUUID":"8019f8f4-21ae-47db-a039-fffbe4dda83f","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"61f8f524-08e5-4d8c-84fd-bbd224e9137a","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LFK8yp4fkKdaF2UCsNUoxb","type":"tool_result","content":"# Analysis of app_lam.py\n\n## Complete Code Overview\n\nThe file is a **Gradio-based web interface** for the LAM (Large Avatar Model) system, enabling one-shot animatable Gaussian head generation from images and motion videos.\n\n## Key Function Definitions\n\n**Core Processing Functions:**\n- `preprocess_fn()` - Handles image preprocessing with background removal and recentering\n- `core_fn()` - Main inference pipeline orchestrating flame tracking, image preparation, motion sequencing, and rendering\n- `save_images2video()` - Converts image sequences to MP4 video\n- `add_audio_to_video()` - Appends audio tracks to generated videos\n\n**Utility Functions:**\n- `parse_configs()` - Loads configuration from YAML and environment variables\n- `_build_model()` - Initializes the LAM model and loads pretrained weights\n- `prepare_working_dir()` - Creates temporary directories for processing\n\n## Gradio Interface Structure\n\nThe interface organizes into four main panels:\n\n1. **Input Image Tab** - Accepts front-facing photographs with preset examples\n2. **Input Video Tab** - Loads motion-driving video sequences\n3. **Processed Image Display** - Shows flame-tracked and segmented results\n4. **Rendered Video Output** - Displays generated animated video\n\n## Audio Integration\n\nThe system processes audio through `add_audio_to_video()`, which accepts a WAV file path corresponding to the motion video and synchronizes it with rendered output using MoviePy's AudioFileClip.\n\n## Notable Observations\n\nNo explicit \"audio2expression\" or \"chatting modules\" imports appear in this file. The audio functionality remains limited to simple synchronization rather than expression synthesis. The optional OpenAvatar Chat export (`enable_oac_file`) generates GLB files but lacks audio-driven animation capabilities."}]},"uuid":"d93ddbe1-acb2-4e42-a81e-3e956de450ff","timestamp":"2026-06-15T21:02:49.495Z","sourceToolAssistantUUID":"61f8f524-08e5-4d8c-84fd-bbd224e9137a","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"d93ddbe1-acb2-4e42-a81e-3e956de450ff","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_015aXisbsH5w5eUpLaNa8Dkw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UnLaWwwLnC8UmYKkovnzvM","name":"WebFetch","input":{"url":"https://raw.githubusercontent.com/aigc3d/LAM/master/app_lam.py","prompt":"Show the FULL raw content of this Python file, including ALL imports at the top, the chatting/audio2expression sections if any, and any optional flags or feature toggles. Do not summarize - show the actual code."},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":959,"cache_read_input_tokens":31305,"output_tokens":139,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":959},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":139,"cache_read_input_tokens":31305,"cache_creation_input_tokens":959,"cache_creation":{"ephemeral_5m_input_tokens":959,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5ipYCHH7aKpYhiGsYkE","attributionAgent":"general-purpose","type":"assistant","uuid":"e9521c1c-93a7-4a7c-bd1e-6012db331159","timestamp":"2026-06-15T21:02:52.947Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"e9521c1c-93a7-4a7c-bd1e-6012db331159","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UnLaWwwLnC8UmYKkovnzvM","type":"tool_result","content":"# Full Raw Content of Python File\n\nThis is a Gradio-based web application for LAM (Large Avatar Model), an Alibaba 3DAIGC project for animating static head images using motion sequences.\n\n## Complete Imports Section\n```python\nimport os\nimport cv2\nimport sys\nimport base64\nimport subprocess\n\nimport gradio as gr\nimport numpy as np\nfrom PIL import Image\nimport argparse\nfrom omegaconf import OmegaConf\n\nimport torch\nimport zipfile\nfrom glob import glob\nimport moviepy.editor as mpy\nfrom lam.utils.ffmpeg_utils import images_to_video\nfrom tools.flame_tracking_single_image import FlameTrackingSingleImage\nfrom lam.runners.infer.head_utils import prepare_motion_seqs, preprocess_image\n\ntry:\n    import spaces\nexcept:\n    pass\n```\n\n## Key Functional Sections\n\n**Processing Pipeline:** The code handles image preprocessing, FLAME facial parameter tracking, motion sequence preparation, and rendering through the `core_fn` function within `demo_lam()`.\n\n**Audio Integration:** Functions include `add_audio_to_video()` which attaches audio tracks to generated video outputs using moviepy.\n\n**Optional Features:**\n- H5 rendering flag (`h5_rendering = False`)\n- Open Avatar Chat export with GLB generation\n- CUDA environment setup\n\n**No explicit audio-to-expression conversion** is present in this file—motion comes from pre-computed FLAME parameters in video sequences.\n\nThe application launches via `launch_gradio_app()`, which initializes the LAM model, flame tracking system, and Gradio interface with example inputs."}]},"uuid":"d37792a5-5c3e-4eac-8edb-9b3868043ad6","timestamp":"2026-06-15T21:02:58.571Z","sourceToolAssistantUUID":"e9521c1c-93a7-4a7c-bd1e-6012db331159","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"28ed936b-8020-4b05-acfe-cfd17b9654f5","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QsEFJYH3ZkSP8DoEMipQVf","type":"tool_result","content":"The server returned HTTP 429 Too Many Requests.\n\nThe response body was not retrieved. If this URL requires authentication, use an authenticated tool (e.g. `gh` for GitHub, or an MCP-provided fetch tool) instead of WebFetch."}]},"uuid":"91cfc1fe-da0d-4ecf-ac72-44e1d9ce3e0e","timestamp":"2026-06-15T21:03:03.537Z","sourceToolAssistantUUID":"28ed936b-8020-4b05-acfe-cfd17b9654f5","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"6516b9e1-d416-4fc5-b3ca-f7e0d7da9bbd","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Ec2cuoVwJn9vLpLb8X2A4V","type":"tool_result","content":"# Analysis of app_lam.py\n\n## Search Results\n\nBased on the provided code, here are my findings:\n\n**Lines mentioning requested functionality:**\n- **audio2expression**: Not found in this file\n- **chatting**: Not found in this file\n- **oac/openavatar**: Present at lines 393-417 (Open Avatar Chat export functionality)\n- **websocket/webrtc/streaming**: Not found in this file\n\n## OpenAvatar Chat Implementation\n\nThe code includes Open Avatar Chat (OAC) support:\n\n> \"if enable_oac_file: try: from tools.generateARKITGLBWithBlender import generate_glb...output_zip_path = os.path.join('./output/open_avatar_chat', base_iid + '.zip')\"\n\nThis creates an OAC export feature (lines 393-417) that generates GLB files and zips them for avatar chatting applications.\n\n## Argument Parser Section\n\nThe argument parser is minimal:\n\n```python\nparser.add_argument(\"--config\", type=str)\nparser.add_argument(\"--infer\", type=str)\nparser.add_argument(\"--blender_path\", type=str,\n    default='blender', help=\"Path to Blender executable\")\n```\n\nOnly three flags are available. Configuration primarily comes from environment variables (`APP_INFER`, `APP_MODEL_NAME`) rather than CLI arguments.\n\n**Key takeaway**: This is a Gradio web UI for avatar generation, not a real-time streaming application."}]},"uuid":"b8b6f909-1426-4e40-88c8-eb5b359e4926","timestamp":"2026-06-15T21:03:08.018Z","sourceToolAssistantUUID":"6516b9e1-d416-4fc5-b3ca-f7e0d7da9bbd","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"76b0c6f4-35c8-4a34-9fa1-6f939ea1728a","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DXq8K3ho9kpaznydbmAwwc","type":"tool_result","content":"# Analysis of generateARKITGLBWithBlender.py\n\n## Code Overview\n\nThe script provided is a **FLAME 3D model processing pipeline** for converting facial models to GLB format. Here's what the code actually does:\n\n### Core Pipeline Steps\n\n1. **Shape Parameter Injection**: Modifies an FBX template with FLAME mesh vertex data\n2. **Format Conversion**: Converts ASCII FBX to binary FBX format using the FBX SDK\n3. **GLB Export**: Uses Blender to convert binary FBX to GLB format\n4. **Vertex Order Generation**: Produces a JSON file mapping vertex indices\n\n### Key Functions\n\n```python\ngenerate_glb(input_mesh, template_fbx, output_glb, blender_exec, cleanup)\n```\n\nThis orchestrates the complete workflow, managing temporary files and cleanup.\n\n## Important Limitations\n\n**The provided code does NOT explicitly show:**\n- OpenAvatarChat format specifications or data structures\n- Audio2Expression integration or connectivity\n- What metadata is embedded in the final GLB file beyond geometry\n\nThe script generates a \".glb\" file and a \"vertex_order.json\" file, but the page content does not detail the internal structure, semantic meaning, or how audio-driven expressions connect to this export pipeline.\n\nThe code references external Blender Python scripts (\"convertFBX2GLB.py\" and \"generateVertexIndices.py\") whose implementation is not included in this file view, so the exact GLB structure remains undocumented here."}]},"uuid":"1577ee2f-fa01-4711-99cc-90220b8009aa","timestamp":"2026-06-15T21:03:16.572Z","sourceToolAssistantUUID":"76b0c6f4-35c8-4a34-9fa1-6f939ea1728a","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"0a5eaad4-0b67-496a-8528-7b6f23ec3af2","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Hmb4vorVL8mU7rkyDYsimn","type":"tool_result","content":"# Pull Request #25 Analysis\n\n## Overview\nThis merged pull request introduces an export feature for OpenAvatarChat in the LAM repository.\n\n**Key Details:**\n- **Status:** Merged on April 30, 2025\n- **Author:** fdyuandong (Collaborator)\n- **Commit:** c2b3728 merged into master branch\n- **Description:** \"No description provided\" - The PR lacks detailed documentation about the feature implementation\n\n## Limitations of Available Information\n\nThe webpage content provided shows minimal substantive details about the actual changes. Notable gaps include:\n\n- No detailed feature description or commit messages visible\n- No code changes displayed (Files changed section not populated in excerpt)\n- No linked issues or related PRs mentioned\n- No reviewer comments or discussion threads captured\n- No specific technical details about export format or integration points\n\n## What's Missing\n\nTo fully understand the OpenAvatarChat export feature, you would need to:\n\n1. Review the actual commit diff (c2b3728)\n2. Check the \"Files changed\" tab for modified code\n3. Look for any linked issues in the development section\n4. Review any branch discussions or review comments\n\nThe PR appears to be a straightforward feature merge without significant discussion or blockers, but comprehensive documentation would require accessing the actual code modifications and related issue tracking."}]},"uuid":"a03f9cbe-4931-40bf-a577-7e990cac748d","timestamp":"2026-06-15T21:03:41.933Z","sourceToolAssistantUUID":"0a5eaad4-0b67-496a-8528-7b6f23ec3af2","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"b25622e1-d1e0-4a5d-a149-8a136eab3129","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017Va5jUcKYxWiK4Z4KeZigZ","type":"tool_result","content":"# Code Changes in Commit c2b3728\n\n## Summary\nThis commit releases an export feature for OpenAvatarChat, enabling users to chat with LAM-generated 3D digital humans. It adds 10 files with 547 additions and 7 deletions.\n\n## Files Modified/Added\n\n### 1. **README.md & README_CN.md**\n- Added language selection toggle (English/中文)\n- Documented new Gradio export feature with link to tools documentation\n- Added usage instructions for Blender path parameter\n\n### 2. **app_lam.py** (+62 lines)\nKey additions:\n- New `--blender_path` command-line argument for Blender executable location\n- `enable_oac_file` checkbox in UI for ZIP export option\n- Export logic generating GLB files and animation data for OpenAvatarChat\n- Creates ZIP archives containing mesh, offset, and animation files\n- New `output_zip_textbox` UI element displaying export file path\n\n### 3. **lam/models/rendering/flame_model/flame.py** (+14 lines)\n- New `save_shaped_mesh()` method saving mesh geometry as OBJ format for export\n\n### 4. **tools/README.md** (NEW, +40 lines)\nInstallation guide covering:\n- FBX SDK 2020.2+ prerequisite setup\n- Blender 4.0.0+ download instructions\n- Template file acquisition from cloud storage\n- Gradio invocation example with Blender path\n\n### 5. **tools/convertFBX2GLB.py** (NEW, +59 lines)\nBlender script converting FBX to GLB format with optimized settings, handling mesh import/export.\n\n### 6. **tools/flame_tracking_single_image.py** (+3 lines)\n- Added `--blender_path` argument support\n\n## Key Feature\nUsers can now generate interactive 3D avatars for real-time conversations on OpenAvatarChat platform by enabling the \"Export ZIP file for Chatting Avatar\" option."}]},"uuid":"08e3d0e9-bc85-4bd9-86e2-a8bf8930c2c4","timestamp":"2026-06-15T21:03:53.256Z","sourceToolAssistantUUID":"b25622e1-d1e0-4a5d-a149-8a136eab3129","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"7779d3f6-2406-447c-9e4e-594c772b648b","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011Y8RN8ux8LQxQvUUx8vY57","type":"tool_result","content":"# FLAME Model FBX/GLB Converter - Full Source Code\n\nThe provided content contains the complete Python source code for Alibaba's 3DAIGC FLAME model converter. Here's a summary of its structure:\n\n## Key Components\n\n**Main Functions:**\n\n1. `update_flame_shape()` - \"Injects FLAME shape parameters into FBX template\" by loading OBJ mesh data and replacing vertex sections in ASCII FBX files\n\n2. `convert_ascii_to_binary()` - Uses the FBX SDK to transform between ASCII and binary formats, managing SDK resources through proper initialization and cleanup\n\n3. `convert_with_blender()` - Executes a Blender subprocess to export FBX files as GLB format using a conversion script\n\n4. `gen_vertex_order_with_blender()` - Generates vertex ordering data via Blender, outputting results to JSON format\n\n5. `generate_glb()` - \"Complete pipeline for FLAME GLB generation\" orchestrating all prior functions sequentially\n\n## Dependencies\n\nThe code requires:\n- FBX SDK (2020.2+)\n- NumPy for numerical operations\n- trimesh for 3D mesh loading\n- Blender executable for final conversions\n\n## Workflow\n\nThe pipeline follows this sequence: shape injection → ASCII-to-binary conversion → Blender GLB export → vertex order generation, with optional cleanup of temporary files."}]},"uuid":"ea3e6f37-c7f0-409b-a783-89ebe0659163","timestamp":"2026-06-15T21:04:07.107Z","sourceToolAssistantUUID":"7779d3f6-2406-447c-9e4e-594c772b648b","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"418ae2c4-01a3-4007-a83b-e37c3d93f990","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01WEac8mnCVozdg1PUb6u29q","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01QrQJnLieEyeeQXbqByJx13","name":"WebFetch","input":{"url":"https://arxiv.org/abs/2404.01523","prompt":"Extract any details about the LAM pipeline, Audio2Expression module, real-time rendering approach, WebGL/WebRTC, browser rendering architecture"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":352,"cache_creation_input_tokens":582,"cache_read_input_tokens":36828,"output_tokens":196,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":582},"inference_geo":"not_available","iterations":[{"input_tokens":352,"output_tokens":196,"cache_read_input_tokens":36828,"cache_creation_input_tokens":582,"cache_creation":{"ephemeral_5m_input_tokens":582,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5ivG3rNy2jah1Sv8gtm","attributionAgent":"general-purpose","type":"assistant","uuid":"aead37f2-42f3-449a-82b1-9c0d0933f300","timestamp":"2026-06-15T21:04:11.447Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"418ae2c4-01a3-4007-a83b-e37c3d93f990","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01ER5iJtiED9nS38BhQM68MR","type":"tool_result","content":"# Blender FBX to GLB Converter - Full Source Code\n\n```python\n\"\"\"\nCopyright (c) 2024-2025, The Alibaba 3DAIGC Team Authors.\n\nBlender FBX to GLB Converter\nConverts 3D models from FBX to glTF Binary (GLB) format with optimized settings.\nRequires Blender to run in background mode.\n\"\"\"\n\nimport bpy\nimport sys\nimport os\nimport json\nfrom pathlib import Path\n\ndef import_obj(filepath):\n    if not os.path.exists(filepath):\n        raise FileNotFoundError(f\"文件不存在：{filepath}\")\n    bpy.ops.wm.obj_import(filepath=filepath)\n    print(f\"成功导入：{filepath}\")\n\n\ndef clean_scene():\n    \"\"\"Clear all objects and data from the current Blender scene\"\"\"\n    bpy.ops.object.select_all(action='SELECT')\n    bpy.ops.object.delete()\n    for collection in [bpy.data.meshes, bpy.data.materials, bpy.data.textures]:\n        for item in collection:\n            collection.remove(item)\n\ndef apply_rotation(obj):\n    obj.rotation_euler = (1.5708, 0, 0)\n    bpy.context.view_layer.update()\n    obj.select_set(True)\n    bpy.context.view_layer.objects.active = obj\n    bpy.ops.object.transform_apply(location=False, rotation=True, scale=False)\n    print(f\"Applied 90-degree rotation to object: {obj.name}\")\n\ndef main():\n    try:\n        argv = sys.argv[sys.argv.index(\"--\") + 1:]\n        input_mesh = Path(argv[0])\n        output_vertex_order_file  = argv[1]\n\n        if not input_mesh.exists():\n            raise FileNotFoundError(f\"Input FBX file not found: {input_mesh}\")\n\n        clean_scene()\n\n        print(f\"Importing {input_mesh}...\")\n        import_obj(str(input_mesh))\n        base_obj = bpy.context.view_layer.objects.active\n\n        apply_rotation(base_obj)\n\n        bpy.context.view_layer.objects.active = base_obj\n        base_obj.select_set(True)\n        bpy.ops.object.mode_set(mode='OBJECT')\n\n        base_objects = [obj for obj in bpy.context.scene.objects if obj.type == 'MESH']\n        if len(base_objects) != 1:\n            raise ValueError(\"Scene should contain exactly one base mesh object.\")\n        base_obj = base_objects[0]\n\n        vertices = [(i, v.co.z) for i, v in enumerate(base_obj.data.vertices)]\n\n        sorted_vertices = sorted(vertices, key=lambda x: x[1])\n        sorted_vertex_indices = [idx for idx, z in sorted_vertices]\n\n        with open(str(output_vertex_order_file), \"w\") as f:\n            json.dump(sorted_vertex_indices, f, indent=4)\n        print(f\"Exported vertex order to: {str(output_vertex_order_file)}\")\n\n\n    except Exception as e:\n        print(f\"Error: {str(e)}\")\n        sys.exit(1)\n\n\nif __name__ == \"__main__\":\n    main()\n```\n\nThis Python script for Blender imports 3D mesh files, applies a 90-degree rotation transformation, and exports vertex indices sorted by their Z-coordinate elevation to a JSON output file."}]},"uuid":"caa94c0a-df03-416d-85a8-1d6eb0e38cdf","timestamp":"2026-06-15T21:04:14.858Z","sourceToolAssistantUUID":"418ae2c4-01a3-4007-a83b-e37c3d93f990","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"aead37f2-42f3-449a-82b1-9c0d0933f300","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QrQJnLieEyeeQXbqByJx13","type":"tool_result","content":"# Response\n\nBased on the webpage content provided, I cannot extract any details about the LAM pipeline, Audio2Expression module, real-time rendering approach, WebGL/WebRTC, or browser rendering architecture.\n\nThe document is an arXiv paper abstract titled \"Proactive Service Assurance in 5G and B5G Networks: A Closed-Loop Algorithm for End-to-End Network Slicing\" by Nguyen Phuc Tran and colleagues. It focuses exclusively on network slicing, service assurance, virtualized network functions (VNFs), and software-defined networking (SDN) for 5G/B5G systems.\n\nNone of the topics you're asking about—LAM pipeline, Audio2Expression, real-time rendering, WebGL/WebRTC, or browser architecture—are mentioned in this networking research paper."}]},"uuid":"80de5dd1-84b1-4e50-8c03-26c8e00c54ef","timestamp":"2026-06-15T21:04:30.323Z","sourceToolAssistantUUID":"aead37f2-42f3-449a-82b1-9c0d0933f300","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"80de5dd1-84b1-4e50-8c03-26c8e00c54ef","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01RjS4mtsVZDJtF19VWGGEbM","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011mLTEgPZRyR3uo4Ydk1icU","name":"WebFetch","input":{"url":"https://github.com/aigc3d/LAM/blob/master/configs/inference/lam-20k-8gpu.yaml","prompt":"Extract the full YAML config showing all model parameters, especially audio2expression settings, expression dimensions, and rendering parameters"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1820,"cache_read_input_tokens":37410,"cache_creation":{"ephemeral_5m_input_tokens":1820,"ephemeral_1h_input_tokens":0},"output_tokens":40,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5iwyG71aA8g76PsKzNn","attributionAgent":"general-purpose","type":"assistant","uuid":"43ec81de-efde-49eb-a18a-ba8a8483d603","timestamp":"2026-06-15T21:04:33.843Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"43ec81de-efde-49eb-a18a-ba8a8483d603","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01RjS4mtsVZDJtF19VWGGEbM","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XzjmwcLYJxewCBwc6uzW13","name":"WebFetch","input":{"url":"https://github.com/aigc3d/LAM/blob/master/lam/runners/infer/utils.py","prompt":"Extract the full Python code, especially motion sequence loading, FLAME parameter structure, and any audio-related utilities"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1820,"cache_read_input_tokens":37410,"cache_creation":{"ephemeral_5m_input_tokens":1820,"ephemeral_1h_input_tokens":0},"output_tokens":40,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5iwyG71aA8g76PsKzNn","attributionAgent":"general-purpose","type":"assistant","uuid":"1f618fb6-fbb0-40dd-b434-e18ddb626042","timestamp":"2026-06-15T21:04:35.044Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"43ec81de-efde-49eb-a18a-ba8a8483d603","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011mLTEgPZRyR3uo4Ydk1icU","type":"tool_result","content":"# LAM Configuration Analysis\n\nBased on the GitHub page content provided, here's the extracted YAML configuration:\n\n## Key Model Parameters\n\n**Expression Dimensions:**\n- `expr_param_dim: 10`\n- `shape_param_dim: 10`\n\n**Rendering Parameters:**\n- `gs_query_dim: 1024`\n- `gs_use_rgb: True`\n- `gs_sh: 3`\n- `gs_xyz_offset_max_step: 0.2`\n- `gs_clip_scaling: 0.01`\n\n**Transformer Architecture:**\n- `transformer_heads: 16`\n- `transformer_dim: 1024`\n- `transformer_layers: 10`\n\n**Image Encoder:**\n- `encoder_type: \"dinov2_fusion\"`\n- `encoder_model_name: \"dinov2_vitl14_reg\"`\n- `encoder_feat_dim: 1024`\n\n## Notable Limitation\n\n**Audio-to-Expression Settings:** The configuration file does not contain any audio-related parameters. There are \"no audio2expression settings\" visible in this inference configuration file—it appears focused on visual-based expression generation rather than audio-driven approaches.\n\nThe configuration supports 20,000-resolution processing across 8 GPUs with a batch size of 4 per GPU and 100 training epochs."}]},"uuid":"9a3d6cfc-0739-47d8-8098-ae307269b4f6","timestamp":"2026-06-15T21:04:38.887Z","sourceToolAssistantUUID":"43ec81de-efde-49eb-a18a-ba8a8483d603","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"1f618fb6-fbb0-40dd-b434-e18ddb626042","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XzjmwcLYJxewCBwc6uzW13","type":"tool_result","content":"# LAM Utils.py Analysis\n\nBased on the GitHub file content provided, here's what I found:\n\n## Core Functionality\n\nThe file contains utility functions for image preprocessing and motion sequence handling in the LAM (Large-scale Appearance Model) project. Key sections include:\n\n**Image Processing Utilities:**\n- `resize_image_keepaspect_np()` - Maintains aspect ratio during resizing\n- `center_crop_according_to_mask()` - Crops images based on mask boundaries\n- `preprocess_image()` - Comprehensive pipeline combining padding, resizing, and masking\n\n**Motion Sequence Loading:**\nThe `prepare_motion_seqs()` function orchestrates the workflow:\n- Loads FLAME parameter files (JSON format)\n- Processes image frames with corresponding parameters\n- Stacks tensors for batch processing\n- Returns structured dictionary with pose, intrinsics, and RGB data\n\n**FLAME Parameter Structure:**\nParameters are stored as dictionaries containing:\n- `betas` (shape parameters)\n- `focal` (focal length)\n- `princpt` (principal point)\n- `img_size_wh` (image dimensions)\n- `R`, `t` (rotation/translation, currently commented)\n\n## Notable Gap\n\n**No audio utilities detected** in this file. The code focuses exclusively on vision-based motion capture preprocessing using FLAME facial model parameters.\n\nThe file lacks any sound/audio processing functionality despite the question's premise."}]},"uuid":"39f9ce0b-3bc0-4bee-80c6-915c2d8b5011","timestamp":"2026-06-15T21:04:40.274Z","sourceToolAssistantUUID":"1f618fb6-fbb0-40dd-b434-e18ddb626042","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"39f9ce0b-3bc0-4bee-80c6-915c2d8b5011","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_0114Fdi3i7BR5Bp8k4zJbYWj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012d1BFkdexSRe8t7Z21nfvF","name":"WebFetch","input":{"url":"https://github.com/aigc3d/LAM/blob/master/lam/runners/infer/utils.py","prompt":"Show the FULL content of prepare_motion_seqs function - every line of code, especially what it reads from disk and what the motion_seqs directory structure looks like"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":974,"cache_read_input_tokens":39230,"output_tokens":133,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":974},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":133,"cache_read_input_tokens":39230,"cache_creation_input_tokens":974,"cache_creation":{"ephemeral_5m_input_tokens":974,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5ixhqwQrfzYrvjDfuTo","attributionAgent":"general-purpose","type":"assistant","uuid":"dff8ec31-4a5d-46c8-ba6d-12b4a6c272d7","timestamp":"2026-06-15T21:04:43.229Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"dff8ec31-4a5d-46c8-ba6d-12b4a6c272d7","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012d1BFkdexSRe8t7Z21nfvF","type":"tool_result","content":"# prepare_motion_seqs Function Analysis\n\n## Function Signature and Overview\n\nThe `prepare_motion_seqs` function processes motion sequences from either a directory of FLAME parameters or from video/image inputs. Here's what it does:\n\n## Input Parameters\n- `motion_seqs_dir`: Directory containing FLAME parameter JSON files\n- `image_folder`: Folder with corresponding image frames\n- `save_root`, `fps`, `bg_color`, `aspect_standard`, `enlarge_ratio`, `render_image_res`, `need_mask`, `multiply`, `vis_motion`: Configuration parameters\n\n## Directory Structure Expected\n\nThe function expects this structure:\n```\nmotion_seqs_dir/\n├── 00000.json  (FLAME parameters)\n├── 00001.json\n├── ...\n└── NNNNN.json\n\nimage_folder/ (optional, parallel structure)\n├── 00000.png or .jpg\n├── 00001.png or .jpg\n└── ...\n```\n\n## Key Operations\n\n1. **Video Processing** (if input is video): Calls `predict_motion_seqs_from_images()` which extracts frames and generates FLAME parameters using external multi-hmr tool\n\n2. **File Reading**: Loads all `.json` files from `motion_seqs_dir`, each containing FLAME facial parameters and metadata\n\n3. **Image Processing**: For each JSON file, reads corresponding image and:\n   - Applies padding via `img_center_padding()`\n   - Resizes while preserving aspect ratio\n   - Crops around face region using `center_crop_according_to_mask()`\n   - Scales intrinsic camera parameters\n\n4. **Output Structure** (returns `motion_seqs` dict):\n   - `render_c2ws`: Camera-to-world matrices [1, N, 4, 4]\n   - `render_intrs`: Intrinsic matrices [1, N, 4, 4]\n   - `render_bg_colors`: Background colors [1, N, 3]\n   - `flame_params`: Stacked FLAME parameters [1, N, ...]\n   - `rgbs`: Processed images [1, N, 3, H, W]\n   - `vis_motion_render`: Optional motion visualization"}]},"uuid":"1e25f2f8-5a48-4409-a5f3-3214d9108a8e","timestamp":"2026-06-15T21:04:51.832Z","sourceToolAssistantUUID":"dff8ec31-4a5d-46c8-ba6d-12b4a6c272d7","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"1e25f2f8-5a48-4409-a5f3-3214d9108a8e","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_014thWreFdAokx6QGdKCwM55","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018JM9rhoBPfa4UqeXAXSxAA","name":"WebFetch","input":{"url":"https://github.com/aigc3d/LAM/blob/master/lam/runners/infer/utils.py?plain=1","prompt":"Show the actual JSON structure with all FLAME parameter keys that get loaded from each JSON file. What keys exist in each frame's JSON? Show any code that processes expression params like expr, jaw_pose, eyes_pose etc."},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":671,"cache_read_input_tokens":40204,"output_tokens":147,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":671},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":147,"cache_read_input_tokens":40204,"cache_creation_input_tokens":671,"cache_creation":{"ephemeral_5m_input_tokens":671,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5iyZEo1FXaTsLetCoPA","attributionAgent":"general-purpose","type":"assistant","uuid":"8f7054cd-b459-481b-abdd-740efdc00b30","timestamp":"2026-06-15T21:04:56.584Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"8f7054cd-b459-481b-abdd-740efdc00b30","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018JM9rhoBPfa4UqeXAXSxAA","type":"tool_result","content":"# FLAME Parameter Structure from LAM Code\n\nBased on the provided code, here's what can be determined about the FLAME JSON structure:\n\n## Keys Loaded from Each Frame's JSON\n\nThe code shows that each JSON file is loaded and processed as:\n\n```python\nflame_param = {k: torch.FloatTensor(v) for k, v in flame_raw_data.items() \n               if \"pad_ratio\" not in k}\n```\n\nThis indicates the JSON contains arbitrary key-value pairs, excluding `pad_ratio`. The code explicitly references these keys:\n- **`focal`** - Camera focal length [x, y]\n- **`princpt`** - Principal point [x, y]\n- **`betas`** - Shape parameters (preserved across all frames from first frame)\n- **`img_size_wh`** - Image dimensions [width, height]\n- **`pad_ratio`** - Padding ratio (excluded from tensor conversion)\n- **`R`** and **`t`** - Rotation matrix and translation (commented out in `_load_pose`)\n\n## Expression Parameters Not Shown\n\nThe code does **not display processing** for expression-related parameters like `expr`, `jaw_pose`, or `eyes_pose`. While these likely exist in the FLAME parameter set (standard in FLAME models), the provided utility file does not contain explicit handling code for these facial expression parameters—they would be bundled into the generic parameter dictionary but remain unprocessed in this excerpt."}]},"uuid":"e8c8811f-818b-4bf1-90ef-8f39110a2a7d","timestamp":"2026-06-15T21:05:02.364Z","sourceToolAssistantUUID":"8f7054cd-b459-481b-abdd-740efdc00b30","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"e8c8811f-818b-4bf1-90ef-8f39110a2a7d","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01BwhCqN9j4iAo8USUGHkBAX","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01C4myAQ85T88DGSwUcF6Q6K","name":"WebFetch","input":{"url":"https://raw.githubusercontent.com/aigc3d/LAM/master/lam/runners/infer/utils.py","prompt":"Show the COMPLETE raw Python source code of this file, line by line. Do not summarize - show all actual code."},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":491,"cache_read_input_tokens":40875,"output_tokens":122,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":491},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":122,"cache_read_input_tokens":40875,"cache_creation_input_tokens":491,"cache_creation":{"ephemeral_5m_input_tokens":491,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5izLFDkStyxzPapYs4z","attributionAgent":"general-purpose","type":"assistant","uuid":"69c97ab9-8ea6-4cb7-a2b0-c58f511e00d2","timestamp":"2026-06-15T21:05:06.319Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"69c97ab9-8ea6-4cb7-a2b0-c58f511e00d2","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01C4myAQ85T88DGSwUcF6Q6K","type":"tool_result","content":"# Complete Python Source Code\n\n```python\nfrom collections import defaultdict\nimport glob\nimport os\nimport json\nimport numpy as np\nfrom PIL import Image\nimport cv2\nimport torch\n\n\ndef scale_intrs(intrs, ratio_x, ratio_y):\n    if len(intrs.shape) >= 3:\n        intrs[:, 0] = intrs[:, 0] * ratio_x\n        intrs[:, 1] = intrs[:, 1] * ratio_y\n    else:\n        intrs[0] = intrs[0] * ratio_x\n        intrs[1] = intrs[1] * ratio_y  \n    return intrs    \n    \ndef calc_new_tgt_size(cur_hw, tgt_size, multiply):\n    ratio = tgt_size / min(cur_hw)\n    tgt_size = int(ratio * cur_hw[0]), int(ratio * cur_hw[1])\n    tgt_size = int(tgt_size[0] / multiply) * multiply, int(tgt_size[1] / multiply) * multiply\n    ratio_y, ratio_x = tgt_size[0] / cur_hw[0], tgt_size[1] / cur_hw[1]\n    return tgt_size, ratio_y, ratio_x\n\ndef calc_new_tgt_size_by_aspect(cur_hw, aspect_standard, tgt_size, multiply):\n    assert abs(cur_hw[0] / cur_hw[1] - aspect_standard) < 0.03\n    tgt_size = tgt_size * aspect_standard, tgt_size\n    tgt_size = int(tgt_size[0] / multiply) * multiply, int(tgt_size[1] / multiply) * multiply\n    ratio_y, ratio_x = tgt_size[0] / cur_hw[0], tgt_size[1] / cur_hw[1]\n    return tgt_size, ratio_y, ratio_x\n    \ndef _load_pose(pose):\n    intrinsic = torch.eye(4)\n    intrinsic[0, 0] = pose[\"focal\"][0]\n    intrinsic[1, 1] = pose[\"focal\"][1]\n    intrinsic[0, 2] = pose[\"princpt\"][0]\n    intrinsic[1, 2] = pose[\"princpt\"][1]\n    intrinsic = intrinsic.float()\n    \n    c2w = torch.eye(4)\n    # c2w[:3, :3] = torch.tensor(pose[\"R\"])\n    # c2w[3, :3] = torch.tensor(pose[\"t\"])\n    c2w = c2w.float()\n    \n    return c2w, intrinsic\n\n\ndef img_center_padding(img_np, pad_ratio):\n    \n    ori_w, ori_h = img_np.shape[:2]\n    \n    w = round((1 + pad_ratio) * ori_w)\n    h = round((1 + pad_ratio) * ori_h)\n    \n    if len(img_np.shape) > 2:\n        img_pad_np = np.zeros((w, h, img_np.shape[2]), dtype=np.uint8)\n    else:\n        img_pad_np = np.zeros((w, h), dtype=np.uint8)\n    offset_h, offset_w = (w - img_np.shape[0]) // 2, (h - img_np.shape[1]) // 2\n    img_pad_np[offset_h: offset_h + img_np.shape[0]:, offset_w: offset_w + img_np.shape[1]] = img_np\n    \n    return img_pad_np\n\n\ndef resize_image_keepaspect_np(img, max_tgt_size):\n    \"\"\"\n    similar to ImageOps.contain(img_pil, (img_size, img_size)) # keep the same aspect ratio  \n    \"\"\"\n    h, w = img.shape[:2]\n    ratio = max_tgt_size / max(h, w)\n    new_h, new_w = round(h * ratio), round(w * ratio)\n    return cv2.resize(img, dsize=(new_w, new_h), interpolation=cv2.INTER_AREA)\n\n\ndef center_crop_according_to_mask(img, mask, aspect_standard, enlarge_ratio):\n    \"\"\" \n        img: [H, W, 3]\n        mask: [H, W]\n    \"\"\" \n    ys, xs = np.where(mask > 0)\n\n    if len(xs) == 0 or len(ys) == 0:\n        raise Exception(\"empty mask\")\n\n    x_min = np.min(xs)\n    x_max = np.max(xs)\n    y_min = np.min(ys)\n    y_max = np.max(ys)\n    \n    center_x, center_y = img.shape[1]//2, img.shape[0]//2\n    \n    half_w = max(abs(center_x - x_min), abs(center_x -  x_max))\n    half_h = max(abs(center_y - y_min), abs(center_y -  y_max))\n    half_w_raw = half_w\n    half_h_raw = half_h\n    aspect = half_h / half_w\n\n    if aspect >= aspect_standard:                \n        half_w = round(half_h / aspect_standard)\n    else:\n        half_h = round(half_w * aspect_standard)\n\n    if half_h > center_y:\n        half_w = round(half_h_raw / aspect_standard)\n        half_h = half_h_raw\n    if half_w > center_x:\n        half_h = round(half_w_raw * aspect_standard)\n        half_w = half_w_raw\n\n    if abs(enlarge_ratio[0] - 1) > 0.01 or abs(enlarge_ratio[1] - 1) >  0.01:\n        enlarge_ratio_min, enlarge_ratio_max = enlarge_ratio\n        enlarge_ratio_max_real = min(center_y / half_h, center_x / half_w)\n        enlarge_ratio_max = min(enlarge_ratio_max_real, enlarge_ratio_max)\n        enlarge_ratio_min = min(enlarge_ratio_max_real, enlarge_ratio_min)\n        enlarge_ratio_cur = np.random.rand() * (enlarge_ratio_max - enlarge_ratio_min) + enlarge_ratio_min\n        half_h, half_w = round(enlarge_ratio_cur * half_h), round(enlarge_ratio_cur * half_w)\n\n    assert half_h <= center_y\n    assert half_w <= center_x\n    assert abs(half_h / half_w - aspect_standard) < 0.03\n    \n    offset_x = center_x - half_w\n    offset_y = center_y - half_h\n    \n    new_img = img[offset_y: offset_y + 2*half_h, offset_x: offset_x + 2*half_w]\n    new_mask = mask[offset_y: offset_y + 2*half_h, offset_x: offset_x + 2*half_w]\n    \n    return  new_img, new_mask, offset_x, offset_y  \n    \n\ndef preprocess_image(rgb_path, mask_path, intr, pad_ratio, bg_color, \n                            max_tgt_size, aspect_standard, enlarge_ratio,\n                            render_tgt_size, multiply, need_mask=True):\n    rgb = np.array(Image.open(rgb_path))\n    rgb_raw = rgb.copy()\n    if pad_ratio > 0:\n        rgb = img_center_padding(rgb, pad_ratio)\n    \n    rgb = rgb / 255.0\n    if need_mask:\n        if rgb.shape[2] < 4:\n            if mask_path is not None:\n                mask = np.array(Image.open(mask_path))\n            else:\n                from rembg import remove\n                mask = remove(rgb_raw[:, :, (2, 1, 0)])[:, :, -1]  # np require [bgr]\n                print(\"rmbg mask: \", mask.min(), mask.max(), mask.shape)\n            if pad_ratio > 0:\n                mask = img_center_padding(mask, pad_ratio)\n            mask = mask / 255.0\n        else:\n            # rgb: [H, W, 4]\n            assert rgb.shape[2] == 4\n            mask = rgb[:, :, 3]   # [H, W]\n    else:\n        # just placeholder\n        mask = np.ones_like(rgb[:, :, 0])\n\n    mask = (mask > 0.5).astype(np.float32)\n    rgb = rgb[:, :, :3] * mask[:, :, None] + bg_color * (1 - mask[:, :, None])\n\n    # resize to specific size require by preprocessor of flame-estimator.\n    rgb = resize_image_keepaspect_np(rgb, max_tgt_size)\n    mask = resize_image_keepaspect_np(mask, max_tgt_size)\n\n    # crop image to enlarge human area.\n    rgb, mask, offset_x, offset_y = center_crop_according_to_mask(rgb, mask, aspect_standard, enlarge_ratio)\n    if intr is not None:\n        intr[0, 2] -= offset_x\n        intr[1, 2] -= offset_y\n\n    # resize to render_tgt_size for training\n    tgt_hw_size, ratio_y, ratio_x = calc_new_tgt_size_by_aspect(cur_hw=rgb.shape[:2], \n                                                                        aspect_standard=aspect_standard,\n                                                                        tgt_size=render_tgt_size, multiply=multiply)\n    rgb = cv2.resize(rgb, dsize=(tgt_hw_size[1], tgt_hw_size[0]), interpolation=cv2.INTER_AREA)\n    mask = cv2.resize(mask, dsize=(tgt_hw_size[1], tgt_hw_size[0]), interpolation=cv2.INTER_AREA)\n    \n    if intr is not None:\n        intr = scale_intrs(intr, ratio_x=ratio_x, ratio_y=ratio_y)\n        assert abs(intr[0, 2] * 2 - rgb.shape[1]) < 2.5, f\"{intr[0, 2] * 2}, {rgb.shape[1]}\"\n        assert abs(intr[1, 2] * 2 - rgb.shape[0]) < 2.5, f\"{intr[1, 2] * 2}, {rgb.shape[0]}\"\n        intr[0, 2] = rgb.shape[1] // 2\n        intr[1, 2] = rgb.shape[0] // 2\n    \n    rgb = torch.from_numpy(rgb).float().permute(2, 0, 1).unsqueeze(0)  # [1, 3, H, W]\n    mask = torch.from_numpy(mask[:, :, None]).float().permute(2, 0, 1).unsqueeze(0)  # [1, 1, H, W]\n    return rgb, mask, intr\n\n\ndef extract_imgs_from_video(video_file, save_root, fps):\n    print(f\"extract_imgs_from_video:{video_file}\")\n    vr = decord.VideoReader(video_file)\n    for i in range(0, len(vr), fps):\n        frame = vr[i].asnumpy()\n        save_path = os.path.join(save_root, f\"{i:05d}.jpg\")\n        cv2.imwrite(save_path, frame[:, :, (2, 1, 0)])\n    \ndef predict_motion_seqs_from_images(image_folder:str, save_root, fps=6):\n    id_name = os.path.splitext(os.path.basename(image_folder))[0]\n    if os.path.isfile(image_folder) and (image_folder.endswith(\"mp4\") or image_folder.endswith(\"move\")):\n        save_frame_root = os.path.join(save_root, \"extracted_frames\", id_name)\n        if not os.path.exists(save_frame_root):\n            os.makedirs(save_frame_root, exist_ok=True)\n            extract_imgs_from_video(video_file=image_folder, save_root=save_frame_root, fps=fps)\n        else:\n            print(\"skip extract_imgs_from_video......\")\n        image_folder = save_frame_root\n\n    image_folder_abspath = os.path.abspath(image_folder)\n    print(f\"predict motion seq:{image_folder_abspath}\")\n    save_flame_root = image_folder + \"_flame_params_mhmr\"\n    if not os.path.exists(save_flame_root):\n        cmd = f\"cd thirdparty/multi-hmr &&  python infer_batch.py  --data_root {image_folder_abspath}  --out_folder {image_folder_abspath} --crop_head   --crop_hand   --pad_ratio 0.2 --smplify\"\n        os.system(cmd)\n    else:\n        print(\"skip predict flame.........\")\n    return save_flame_root, image_folder\n    \n\ndef prepare_motion_seqs(motion_seqs_dir, image_folder, save_root, fps,\n                        bg_color, aspect_standard, enlarge_ratio,\n                        render_image_res, need_mask, multiply=16, \n                        vis_motion=False):\n    if motion_seqs_dir is None:\n        assert image_folder is not None\n        motion_seqs_dir, image_folder = predict_motion_seqs_from_images(image_folder, save_root, fps)\n    \n    motion_seqs = sorted(glob.glob(os.path.join(motion_seqs_dir, \"*.json\")))\n    \n    # source images\n    c2ws, intrs, rgbs, bg_colors, masks = [], [], [], [], []\n    flame_params = []\n    shape_param = None\n        \n    for idx, flame_path in enumerate(motion_seqs):\n        if image_folder is not None:\n            file_name = os.path.splitext(os.path.basename(flame_path))[0]\n            frame_path = os.path.join(image_folder, file_name + \".png\")\n            if not os.path.exists(frame_path):\n                frame_path = os.path.join(image_folder, file_name + \".jpg\")\n                    \n        with open(flame_path) as f:\n            flame_raw_data = json.load(f)\n            flame_param = {k: torch.FloatTensor(v) for k, v in flame_raw_data.items() if \"pad_ratio\" not in k}\n        \n        if idx == 0:\n            shape_param = flame_param[\"betas\"]\n\n        c2w, intrinsic = _load_pose(flame_param)\n        intrinsic_raw = intrinsic.clone()\n        if image_folder is not None:\n            rgb, mask, intrinsic = preprocess_image(frame_path, mask_path=None,\n                                                    need_mask=need_mask,\n                                                    bg_color=bg_color, \n                                                    pad_ratio=float(flame_raw_data[\"pad_ratio\"]),\n                                                    max_tgt_size=int(flame_param[\"img_size_wh\"][0]),\n                                                    aspect_standard=aspect_standard,\n                                                    enlarge_ratio=enlarge_ratio,\n                                                    render_tgt_size=render_image_res,\n                                                    multiply=multiply,\n                                                    intr=intrinsic)\n            rgbs.append(rgb)\n            masks.append(mask)\n            \n        c2ws.append(c2w)\n        bg_colors.append(bg_color)\n        intrs.append(intrinsic)\n        # intrs.append(intrinsic_raw)\n        flame_params.append(flame_param)\n\n    c2ws = torch.stack(c2ws, dim=0)  # [N, 4, 4]\n    intrs = torch.stack(intrs, dim=0)  # [N, 4, 4]\n    bg_colors = torch.tensor(bg_colors, dtype=torch.float32).unsqueeze(-1).repeat(1, 3)  # [N, 3]\n\n    if len(rgbs) > 0:\n        rgbs = torch.cat(rgbs, dim=0)  # [N, 3, H, W]\n        # masks = torch.cat(masks, dim=0)  # [N, 1, H, W]\n\n    flame_params_tmp = defaultdict(list)\n    for flame in flame_params:\n        for k, v in flame.items():\n            flame_params_tmp[k].append(v)\n    for k, v in flame_params_tmp.items():\n        flame_params_tmp[k] = torch.stack(v)  # [Nv, xx, xx]\n    flame_params = flame_params_tmp\n    # TODO check different betas for same person\n    flame_params[\"betas\"] = shape_param  \n    \n    if vis_motion:\n        motion_render = render_flame_mesh(flame_params, intrs)\n    else:\n        motion_render = None\n\n    # add batch dim\n    for k, v in flame_params.items():\n        flame_params[k] = v.unsqueeze(0)\n        # print(k, flame_params[k].shape, \"motion_seq\")\n    c2ws = c2ws.unsqueeze(0)    \n    intrs = intrs.unsqueeze(0)\n    bg_colors = bg_colors.unsqueeze(0)\n    if len(rgbs) > 0:\n        rgbs = rgbs.unsqueeze(0)\n    # print(f\"c2ws:{c2ws.shape}, intrs:{intrs.shape}, rgbs:{rgbs.shape if len(rgbs) > 0 else None}\")\n    \n    motion_seqs = {}\n    motion_seqs[\"render_c2ws\"] = c2ws\n    motion_seqs[\"render_intrs\"] = intrs\n    motion_seqs[\"render_bg_colors\"] = bg_colors\n    motion_seqs[\"flame_params\"] = flame_params\n    motion_seqs[\"rgbs\"] = rgbs\n    motion_seqs[\"vis_motion_render\"] = motion_render\n\n    return motion_seqs\n```"}]},"uuid":"c3553a0a-5ea1-43b3-aa54-104fd5b46d9a","timestamp":"2026-06-15T21:05:35.146Z","sourceToolAssistantUUID":"69c97ab9-8ea6-4cb7-a2b0-c58f511e00d2","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"0be422ef-bd1b-4dc4-a897-4a48f4bd2a0f","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Twps53azPvJrWk2xWQc55z","type":"tool_result","content":"# GS3DRenderer Key Signatures\n\n## Constructor\n```python\ndef __init__(self, \n    human_model_path, subdivide_num, smpl_type, feat_dim, query_dim,\n    use_rgb, sh_degree, xyz_offset_max_step, mlp_network_config,\n    expr_param_dim, shape_param_dim,\n    clip_scaling=0.2, scale_sphere=False, skip_decoder=False,\n    fix_opacity=False, fix_rotation=False,\n    decode_with_extra_info=None, gradient_checkpointing=False,\n    add_teeth=True, teeth_bs_flag=False, oral_mesh_flag=False, **kwargs)\n```\n\n## Forward Method\n```python\ndef forward(self,\n    gs_hidden_features: Float[Tensor, \"B Np Cp\"],\n    query_points: Float[Tensor, \"B Np 3\"],\n    flame_data,\n    c2w: Float[Tensor, \"B Nv 4 4\"],\n    intrinsic: Float[Tensor, \"B Nv 4 4\"],\n    height, width,\n    additional_features: Optional[Float[Tensor, \"B C H W\"]] = None,\n    background_color: Optional[Float[Tensor, \"B Nv 3\"]] = None,\n    debug: bool = False, **kwargs)\n```\n\n## Animate GS Model Method\n```python\ndef animate_gs_model(self, \n    gs_attr: GaussianModel, \n    query_points,  # [N, 3]\n    flame_data, \n    debug=False)\n```\n\n## FLAME Data Keys Used\n- `expr`: [B, Nv, 100] - expression parameters\n- `teeth_bs`: [B, Nv, X] - teeth blendshapes (if teeth_bs_flag=True)\n- `betas`: [B, 100] - shape parameters\n- `rotation`: [B, Nv, 3] - global rotation\n- `neck_pose`: [B, Nv, 3] - neck rotation\n- `jaw_pose`: [B, Nv, 3] - jaw rotation\n- `eyes_pose`: [B, Nv, 6] - eye rotations\n- `translation`: [B, Nv, 3] - translation\n\nReturns dictionary with keys: `comp_rgb` [B, Nv, C, H, W], `comp_mask`, `comp_depth`, `3dgs`"}]},"uuid":"c8168a73-23c7-4c1d-bbaa-1978921fa141","timestamp":"2026-06-15T21:05:50.941Z","sourceToolAssistantUUID":"0be422ef-bd1b-4dc4-a897-4a48f4bd2a0f","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"3c789ba3-8640-4c85-a817-1f3e3a4da9cb","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018YL7rrc1tkQPKKQ8YCTGEn","type":"tool_result","content":"# Public Repositories for aigc3d Organization\n\nBased on the webpage content, here are the publicly listed repositories:\n\n## Pinned Repositories\n1. **LHM** - Large Animatable Human Reconstruction Model (ICCV 2025)\n2. **LAM** - Large Avatar Model for One-shot Animatable Gaussian Head (SIGGRAPH 2025)\n3. **LAM_Audio2Expression** - ARKit expression generation from audio\n4. **LAM_WebRender** - WebGL renderer for LAM projects\n5. **motionshop** - Human motion replacement in videos\n6. **LHM-plusplus** - Efficient Large Human Reconstruction Model\n\n## Additional Public Repositories\n7. **ViGeo** - Video Geometry Estimation\n8. **LDCM** - Large Depth Completion Model (ICLR 2026)\n9. **HyPlaneHead** - Tri-plane-like Representations in Full-Head Synthesis (NeurIPS 2025)\n10. **motionshop-2** - Advanced version with 3D Animation Engine\n11. **.github** - Organization profile repository\n\nThe page indicates \"Showing 10 of 27 repositories,\" suggesting additional repositories exist but aren't fully detailed in this view."}]},"uuid":"135f824f-d670-45c1-b15b-4be5eecabbc7","timestamp":"2026-06-15T21:06:29.582Z","sourceToolAssistantUUID":"3c789ba3-8640-4c85-a817-1f3e3a4da9cb","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"8c9f3426-b7b3-46e5-80f8-7bf07d037acf","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JKtJW4cs6uiJy32NBbz733","type":"tool_result","content":"# LAM README Summary\n\n## OpenAvatarChat Integration\n\nThe README references OpenAvatarChat multiple times as a platform for interactive chatting with LAM-generated avatars. The exact GitHub URL provided is: **https://github.com/HumanAIGC-Engineering/OpenAvatarChat**\n\nKey mentions include:\n- \"Avatar Export Feature that allows users to chat with any LAM-generated 3D digital humans on OpenAvatarChat\"\n- Released WebGL Interactive Chatting Avatar SDK on OpenAvatarChat with \"LLM, ASR, TTS, Avatar\" components\n\n## WebGL Render SDK\n\nThe README states: **\"We have released the WebGL-Render!\"** (May 20, 2025)\n\nThe WebGL render is available at: **https://github.com/aigc3d/LAM_WebRender**\n\nThis enables \"cross-platform animation and rendering\" capabilities for LAM avatars.\n\n## Chatting Avatar Demo\n\nThe project includes an interactive demo where users can \"freely chat with the 3D Digital Human generated by LAM\" through the OpenAvatarChat platform. Online demos are available on:\n- HuggingFace Spaces\n- ModelScope\n\nThe SDK supports real-time conversation features combining language models, speech recognition, text-to-speech, and avatar animation."}]},"uuid":"043c0a33-4d37-4ef0-8f07-684b8f8666f5","timestamp":"2026-06-15T21:06:30.368Z","sourceToolAssistantUUID":"8c9f3426-b7b3-46e5-80f8-7bf07d037acf","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"043c0a33-4d37-4ef0-8f07-684b8f8666f5","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01GBY36Z3dw3w41AFheJVJQm","type":"message","role":"assistant","content":[{"type":"text","text":"The correct URL is `HumanAIGC-Engineering/OpenAvatarChat`, not `aigc3d/OpenAvatarChat`. Let me fetch those repos now."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":913,"cache_read_input_tokens":49564,"cache_creation":{"ephemeral_5m_input_tokens":913,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5j6pchmwQwHRZtiiFYQ","attributionAgent":"general-purpose","type":"assistant","uuid":"5142c90e-d986-4718-bfa5-6b734470c936","timestamp":"2026-06-15T21:06:33.713Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"5142c90e-d986-4718-bfa5-6b734470c936","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01GBY36Z3dw3w41AFheJVJQm","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RjxQfH4yX3FVUHTsG3yZrJ","name":"WebFetch","input":{"url":"https://github.com/HumanAIGC-Engineering/OpenAvatarChat","prompt":"Extract all content including: repository structure, README, pipeline description, audio input/output format, WebRTC/WebSocket details, browser rendering approach, Python API, LAM integration details, key files and classes"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":913,"cache_read_input_tokens":49564,"cache_creation":{"ephemeral_5m_input_tokens":913,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5j6pchmwQwHRZtiiFYQ","attributionAgent":"general-purpose","type":"assistant","uuid":"51004b3e-2d6b-4b71-a915-ca2f256e42bd","timestamp":"2026-06-15T21:06:35.666Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"51004b3e-2d6b-4b71-a915-ca2f256e42bd","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01GBY36Z3dw3w41AFheJVJQm","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019eKTaAxVL9twdjq6KBQZ2s","name":"WebFetch","input":{"url":"https://github.com/aigc3d/LAM_Audio2Expression","prompt":"Extract all content: README, pipeline, input/output format of Audio2Expression, Python API, how audio is consumed (WAV? streaming?), what FLAME parameters are output, how to use programmatically"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":913,"cache_read_input_tokens":49564,"cache_creation":{"ephemeral_5m_input_tokens":913,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5j6pchmwQwHRZtiiFYQ","attributionAgent":"general-purpose","type":"assistant","uuid":"14771864-563f-4861-a007-99bfd48b5692","timestamp":"2026-06-15T21:06:37.081Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"14771864-563f-4861-a007-99bfd48b5692","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019eKTaAxVL9twdjq6KBQZ2s","type":"tool_result","content":"# LAM-A2E: Audio to Expression - Technical Summary\n\n## Overview\nLAM-A2E is a real-time system that \"leverages audio input to generate ARKit blendshapes-driven facial expressions\" for 3D avatar animation.\n\n## Architecture & Pipeline\nThe system employs an encoder-decoder architecture:\n- **Audio Encoder**: Wav2Vec (pre-trained speech model)\n- **Processing**: Extracts audio features combined with style features\n- **Output**: Decoder generates stylized blendshape coefficients\n\n## Input/Output Format\n\n**Input:**\n- Audio files (WAV format supported, as evidenced by sample: `BarackObama_english.wav`)\n- Streaming audio support via `inference_streaming_audio.py`\n\n**Output:**\n- ARKit blendshape coefficients adapted to FLAME facial topology\n- Real-time expression parameters for 3D avatar animation\n\n## Python API & Usage\n\n**Quick Start (Gradio Interface):**\n```bash\npython app_lam_audio2exp.py\n```\n\n**Inference Command:**\n```bash\npython inference.py --config-file ${CONFIG_PATH} --options save_path=${SAVE_PATH} weight=${CHECKPOINT_PATH} audio_input=${AUDIO_INPUT}\n```\n\n## Programmatic Usage\n- Configuration files: Located in `configs/` directory\n- Weights: Pre-trained models available for download\n- The system supports both batch inference and streaming audio processing\n\n## Dependencies\n- Python 3.10 (required)\n- CUDA 12.1 or 11.8\n- Standard ML frameworks (PyTorch implied)\n\n## Model Access\nAvailable via HuggingFace, OSS, and ModelScope platforms."}]},"uuid":"cd32fdd9-b03a-4c10-89ac-ee9750f0a343","timestamp":"2026-06-15T21:06:43.470Z","sourceToolAssistantUUID":"14771864-563f-4861-a007-99bfd48b5692","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"51004b3e-2d6b-4b71-a915-ca2f256e42bd","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RjxQfH4yX3FVUHTsG3yZrJ","type":"tool_result","content":"# OpenAvatarChat Repository Analysis\n\n## Overview\nOpenAvatarChat is a \"modular interactive digital human dialogue implementation\" supporting multimodal interactions (text, speech, video) with pluggable ASR, LLM, TTS, and avatar components.\n\n## Repository Structure\n```\n├── src/                    # Core source code\n├── config/                 # Configuration files for different modes\n├── models/                 # Model-related code\n├── resource/avatar/        # Avatar resources\n├── extensions/openclaw/    # Chat Agent integration\n├── scripts/                # Utility scripts (model downloads)\n├── build/                  # Build artifacts\n├── docs/                   # Documentation\n└── docker-compose.yml      # Containerization setup\n```\n\n## Core Architecture\n\n**Key Strengths:**\n- \"Modular architecture design\" enabling flexible component swapping\n- Support for multiple avatar technologies: LiteAvatar, LAM, MuseTalk, FlashHead\n- Average response time optimized to \"2.2 seconds\" through VAD detection and frame rate control\n\n## Audio Pipeline\n\n**Input/Output:**\n- ASR: SenseVoice integration for speech recognition\n- TTS: CosyVoice API support for speech synthesis\n- VAD: Silero-VAD for voice activity detection\n- Audio buffering and frame rate optimization mechanisms\n\n## Component Dependencies\n\n| Component | Library | Purpose |\n|-----------|---------|---------|\n| RTC | gradio-webrtc | Real-time communication |\n| WebUI | OpenAvatarChat-WebUI | Frontend interface |\n| VAD | silero-vad | Voice detection |\n| Avatar | LiteAvatar, LAM, MuseTalk, FlashHead | Visual generation |\n| TTS | CosyVoice | Speech synthesis |\n\n## LAM Integration\nLAM (Audio2Expression) processes audio to facial expressions, utilizing wav2vec2-base-960h for feature extraction. Supports expression-driven 3D avatar animations.\n\n## Browser Rendering\nFrontend uses WebRTC for real-time streaming and Gradio for web interface. Separate repository (OpenAvatarChat-WebUI) handles modern UI with potential Live2D alternative rendering support.\n\n## Configuration Modes\nSeven preset modes including:\n- Chat with LiteAvatar + SenseVoice + API LLM\n- Qwen-Omni multimodal setup\n- FlashHead with duplex (bidirectional) interruption support\n- Beta: Chat Agent mode with tool calling\n\n## Key Technologies\n- **Language:** Python (96.8%)\n- **Framework:** Gradio-based web interface\n- **Deployment:** Docker support with CUDA optimization\n- **License:** Apache 2.0\n\n## Latest Release (v0.6.0)\nArchitecture refactored with frontend/backend separation, universal interruption support, and SoulX-FlashHead diffusion-based avatar integration."}]},"uuid":"d223e5a6-9463-43bb-ade9-ad8cf18c1d4d","timestamp":"2026-06-15T21:06:45.198Z","sourceToolAssistantUUID":"51004b3e-2d6b-4b71-a915-ca2f256e42bd","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"d223e5a6-9463-43bb-ade9-ad8cf18c1d4d","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01PQfsQk6BZHMqhECRA5VmTz","type":"message","role":"assistant","content":[{"type":"text","text":"Excellent! Now I have the critical information. Let me gather the remaining specifics from the Audio2Expression and OpenAvatarChat repos."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":425,"cache_creation_input_tokens":1086,"cache_read_input_tokens":50477,"cache_creation":{"ephemeral_5m_input_tokens":1086,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5j7vdNwNq5y1fL4FpFd","attributionAgent":"general-purpose","type":"assistant","uuid":"00be264a-d9a2-4406-a2f7-68ef319b4708","timestamp":"2026-06-15T21:06:47.677Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"ea802051-4d76-451f-9cb7-66620f28a3dd","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01PQfsQk6BZHMqhECRA5VmTz","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01QnVApJanNu5P5vhAHri24J","name":"WebFetch","input":{"url":"https://api.github.com/repos/HumanAIGC-Engineering/OpenAvatarChat/git/trees/main?recursive=1","prompt":"List ALL file paths in the repository"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":425,"cache_creation_input_tokens":1086,"cache_read_input_tokens":50477,"output_tokens":230,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1086},"inference_geo":"not_available","iterations":[{"input_tokens":425,"output_tokens":230,"cache_read_input_tokens":50477,"cache_creation_input_tokens":1086,"cache_creation":{"ephemeral_5m_input_tokens":1086,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5j7vdNwNq5y1fL4FpFd","attributionAgent":"general-purpose","type":"assistant","uuid":"6a0de276-c235-4c49-8485-cbf957a1b084","timestamp":"2026-06-15T21:06:49.625Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"ea802051-4d76-451f-9cb7-66620f28a3dd","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MQGDFbzWtzVQmbSkrxVBVJ","type":"tool_result","content":"# File Paths in LAM_Audio2Expression Repository\n\n## Root Level Files\n- `.gitignore`\n- `LICENSE`\n- `README.md`\n- `app_lam_audio2exp.py`\n- `inference.py`\n- `inference_streaming_audio.py`\n- `requirements.txt`\n\n## Assets Directory\n- `assets/images/framework.png`\n- `assets/images/logo.jpeg`\n- `assets/images/snapshot.png`\n- `assets/images/teaser.jpg`\n\n## Configs Directory\n- `configs/lam_audio2exp_config.py`\n- `configs/lam_audio2exp_config_streaming.py`\n- `configs/wav2vec2_config.json`\n\n## Engines Directory\n- `engines/__init__.py`\n- `engines/defaults.py`\n- `engines/infer.py`\n- `engines/launch.py`\n- `engines/train.py`\n- `engines/hooks/__init__.py`\n- `engines/hooks/builder.py`\n- `engines/hooks/default.py`\n- `engines/hooks/evaluator.py`\n- `engines/hooks/misc.py`\n\n## Models Directory\n- `models/__init__.py`\n- `models/builder.py`\n- `models/default.py`\n- `models/network.py`\n- `models/utils.py`\n- `models/encoder/wav2vec.py`\n- `models/encoder/wavlm.py`\n- `models/losses/__init__.py`\n- `models/losses/builder.py`\n- `models/losses/lovasz.py`\n- `models/losses/misc.py`\n\n## Scripts Directory\n- `scripts/install/install_cu118.sh`\n- `scripts/install/install_cu121.sh`\n\n## Utils Directory\n- `utils/__init__.py`\n- `utils/cache.py`\n- `utils/comm.py`\n- `utils/config.py`\n- `utils/env.py`\n- `utils/events.py`\n- `utils/logger.py`\n- `utils/misc.py`\n- `utils/optimizer.py`\n- `utils/path.py`\n- `utils/registry.py`\n- `utils/scheduler.py`\n- `utils/timer.py`\n- `utils/visualization.py`\n\n## Wheels Directory\n- `wheels/gradio_gaussian_render-0.0.3-py3-none-any.whl`"}]},"uuid":"c7bdf543-df10-4cf6-9827-9564aaf76d8b","timestamp":"2026-06-15T21:06:54.717Z","sourceToolAssistantUUID":"ea802051-4d76-451f-9cb7-66620f28a3dd","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"6a0de276-c235-4c49-8485-cbf957a1b084","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QnVApJanNu5P5vhAHri24J","type":"tool_result","content":"# Complete File Paths in OpenAvatarChat Repository\n\n## Root Configuration Files\n- `.dockerignore`\n- `.gitattributes`\n- `.gitignore`\n- `.gitmodules`\n- `Dockerfile`\n- `LICENSE`\n- `README.md`\n- `readme_en.md`\n- `pyproject.toml`\n- `setup.cfg`\n- `docker-compose.yml`\n- `install.py`\n- `build_cuda128.sh`\n- `run_docker_cuda128.sh`\n\n## Directory Structure\n\n### Configuration Directory (`config/`)\n- `chat_with_lam.yaml`\n- `chat_with_lam_bailian_asr_duplex.yaml`\n- `chat_with_lam_duplex.yaml`\n- `chat_with_openai_compatible.yaml`\n- `chat_with_openai_compatible_bailian_cosyvoice.yaml`\n- `chat_with_openai_compatible_bailian_cosyvoice_duplex.yaml`\n- `chat_with_openai_compatible_bailian_cosyvoice_flashhead.yaml`\n- `chat_with_openai_compatible_bailian_cosyvoice_flashhead_duplex.yaml`\n- `chat_with_openai_compatible_bailian_cosyvoice_flashhead_duplex_agent.yaml`\n- `chat_with_openai_compatible_bailian_cosyvoice_musetalk.yaml`\n- `chat_with_openai_compatible_bailian_cosyvoice_musetalk_duplex.yaml`\n- `chat_with_openai_compatible_edge_tts.yaml`\n- `chat_with_qwen_omni.yaml`\n\n### Documentation (`docs/`)\n- `FAQ.md`\n- `index.md`\n- `package.json`\n- `package-lock.json`\n\n#### VitePress Configuration\n- `.vitepress/config.mts`\n\n#### Community Section\n- `community/index.md`\n- `community/faq.md`\n\n#### Getting Started Section\n- `getting-started/index.md`\n- `getting-started/docker.md`\n- `getting-started/flashhead.md`\n- `getting-started/lam.md`\n- `getting-started/liteavatar.md`\n- `getting-started/musetalk.md`\n\n#### Guide Section\n- `guide/introduction.md`\n- `guide/how-it-works.md`\n- `guide/deployment.md`\n\n#### Reference Section\n- `reference/configuration.md`\n- `reference/preset-modes.md`\n\n#### Reference: Handlers\n- `reference/handlers/index.md`\n\n**Agent Handlers:**\n- `reference/handlers/agent/chat-agent.md`\n\n**ASR Handlers:**\n- `reference/handlers/asr/sensevoice.md`\n\n**Avatar Handlers:**\n- `reference/handlers/avatar/flashhead.md`\n- `reference/handlers/avatar/lam.md`\n- `reference/handlers/avatar/liteavatar.md`\n- `reference/handlers/avatar/musetalk.md`\n\n**Client Handlers:**\n- `reference/handlers/client/lam-client.md`\n- `reference/handlers/client/rtc-client.md`\n\n**LLM Handlers:**\n- `reference/handlers/llm/dify.md`\n- `reference/handlers/llm/openai-compatible.md`\n- `reference/handlers/llm/qwen-omni.md`\n\n**Manager Handlers:**\n- `reference/handlers/manager/data-tool.md`\n\n**TTS Handlers:**\n- `reference/handlers/tts/bailian-cosyvoice.md`\n- `reference/handlers/tts/cosyvoice-local.md`\n- `reference/handlers/tts/edge-tts.md`\n\n**VAD Handlers:**\n- `reference/handlers/vad/silero-vad.md`\n- `reference/handlers/vad/smart-turn.md`\n\n#### Releases\n- `releases/release-notes.md`\n\n#### English Documentation (en/)\nSame structure repeated with additional content in `en/` subdirectory\n\n#### Beta Features\n- `beta/index.md`\n- `beta/chat-agent.md`\n\n### Assets Directory\n- `assets/images/bilibili_video.jpg`\n- `assets/images/community_wechat.png`\n- `assets/images/data_flow.svg`\n- `assets/images/modelscope_logo.png`\n\n### Build Directory\n- `build/components_will_be_built_here.txt`\n\n### COTURN Data\n- `coturn-data/turnserver.conf`\n\n### Extensions\n- `extensions/openclaw/oac-bridge/index.ts`\n- `extensions/openclaw/oac-bridge/package.json`\n- `extensions/openclaw/oac-bridge/openclaw.plugin.json`\n- `extensions/openclaw/oac-bridge/src/accounts.ts`\n- `extensions/openclaw/oac-bridge/src/channel.ts`\n- `extensions/openclaw/oac-bridge/src/gateway-runtime.ts`\n- `extensions/openclaw/oac-bridge/src/inbound-context.ts`\n- `extensions/openclaw/oac-bridge/src/inbound-turn.ts`\n- `extensions/openclaw/oac-bridge/src/profile-tool.ts`\n- `extensions/openclaw/oac-bridge/src/runtime.ts`\n- `extensions/openclaw/oac-bridge/src/session-key.ts`\n- `extensions/openclaw/oac-bridge/src/tasks-tool.ts`\n- `extensions/openclaw/oac-bridge/src/types.ts`\n\n### Models Directory\n- `models/put_models_here.txt`\n\n### Resources\n- `resource/avatar/put_avatar_here.txt`\n- `resource/avatar/flashhead/girl.png`\n\n### Scripts\n- `scripts/create_ssl_certs.sh`\n- `scripts/download_avatar_model.py`\n- `scripts/download_liteavatar_weights.sh`\n- `scripts/download_models.py`\n- `scripts/download_musetalk_weights.sh`\n- `scripts/download_smart_turn_weights.sh`\n- `scripts/setup_coturn.sh`\n\n### Source Code (`src/`)\n\n#### Main Demo\n- `demo.py`\n\n#### Chat Engine\n- `chat_engine/__init__.py`\n- `chat_engine/chat_engine.py`\n\n**Common Modules:**\n- `chat_engine/common/client_handler_base.py`\n- `chat_engine/common/handler_base.py`\n- `chat_engine/common/logic_base.py`\n\n**Contexts:**\n- `chat_engine/contexts/common_module_context.py`\n- `chat_engine/contexts/handler_context.py`\n- `chat_engine/contexts/logic_context.py`\n- `chat_engine/contexts/session_clock.py`\n- `chat_engine/contexts/session_context.py`\n- `chat_engine/contexts/session_history.py`\n\n**Core:**\n- `chat_engine/core/chat_session.py`\n- `chat_engine/core/handler_manager.py`\n- `chat_engine/core/logic_manager.py`\n- `chat_engine/core/signal_filter.py`\n- `chat_engine/core/signal_manager.py`\n- `chat_engine/core/stream_filter.py`\n- `chat_engine/core/stream_manager.py`\n\n**Data Models:**\n- `chat_engine/data_models/chat_data_type.py`\n- `chat_engine/data_models/chat_engine_config_data.py`\n- `chat_engine/data_models/chat_signal.py`\n- `chat_engine/data_models/chat_signal_type.py`\n- `chat_engine/data_models/chat_stream.py`\n- `chat_engine/data_models/chat_stream_config.py`\n- `chat_engine/data_models/chat_stream_status.py`\n- `chat_engine/data_models/engine_channel_type.py`\n- `chat_engine/data_models/session_info_data.py`\n\n**Chat Data Models:**\n- `chat_engine/data_models/chat_data/chat_data_model.py`\n\n**Internal Data Models:**\n- `chat_engine/data_models/internal/chat_data_endpoints.py`\n- `chat_engine/data_models/internal/handler_definition_data.py`\n- `chat_engine/data_models/internal/handler_session_data.py`\n- `chat_engine/data_models/internal/logic_definition_data.py`\n- `chat_engine/data_models/internal/logic_session_data.py`\n\n**Runtime Data Models:**\n- `chat_engine/data_models/runtime_data/data_bundle.py`\n- `chat_engine/data_models/runtime_data/data_store.py`\n- `chat_engine/data_models/runtime_data/event_model.py`\n- `chat_engine/data_models/runtime_data/motion_data.py`\n- `chat_engine/data_models/runtime_data/motion_data_descriptors.py`\n- `chat_engine/data_models/runtime_data/motion_entry_serializer_base.py`\n- `chat_engine/data_models/runtime_data/motion_entry_serializers/int16_audio_serializer.py`\n- `chat_engine/data_models/runtime_data/time_unit_type.py`\n\n#### Engine Utilities\n- `engine_utils/directory_info.py`\n- `engine_utils/general_slicer.py`\n- `engine_utils/import_utils.py`\n- `engine_utils/inspect_utils.py`\n- `engine_utils/interval_counter.py`\n- `engine_utils/media_utils.py`\n- `engine_utils/path_utils.py`\n- `engine_utils/singleton.py`\n- `engine_utils/text_filter.py`\n- `engine_utils/time_utils.py`\n\n**Audio Utilities:**\n- `engine_utils/audio_utils/auto_gain_control.py`\n- `engine_utils/audio_utils/common_audio_utils.py`\n\n#### Handlers\n\n**Agent Handler:**\n- `handlers/agent/agent_data_models.py`\n- `handlers/agent/chat_agent_handler.py`\n- `handlers/agent/pyproject.toml`\n\n**Agent Memory:**\n- `handlers/agent/memory/perception_buffer.py`\n- `handlers/agent/memory/session_memory_manager.py`\n- `handlers/agent/memory/session_summary.py`\n- `handlers/agent/memory/working_memory.py`\n- `handlers/agent/memory/write_behind_queue.py`\n\n**Agent OpenClaw Bridge:**\n- `handlers/agent/oc_bridge/mcp_client.py`\n- `handlers/agent/oc_bridge/memory_retriever.py`\n- `handlers/agent/oc_bridge/oc_bridge_init.py`\n- `handlers/agent/oc_bridge/oc_channel_client.py`\n- `handlers/agent/oc_bridge/oc_prompt_injection.py`\n- `handlers/agent/oc_bridge/oc_reply_bridge.py`\n- `handlers/agent/oc_bridge/oc_tools.py`\n- `handlers/agent/oc_bridge/pending_confirmations.py`\n- `handlers/agent/oc_bridge/persona_snapshot_mgr.py`\n- `handlers/agent/oc_bridge/task_mirror.py`\n- `handlers/agent/oc_bridge/task_notification_queue.py`\n\n**Agent Perception:**\n- `handlers/agent/perception/perception_handler.py`\n- `handlers/agent/perception/vision_model_interface.py`\n\n**Agent Prompt:**\n- `handlers/agent/prompt/prompt_compiler.py`\n\n**Agent Scheduler:**\n- `handlers/agent/scheduler/scheduler_layer.py`\n\n**Agent Tools:**\n- `handlers/agent/tools/base_tool.py`\n- `handlers/agent/tools/demo_tools.py`\n- `handlers/agent/tools/exec_approve.py`\n- `handlers/agent/tools/pending_confirmations_tool.py`\n- `handlers/agent/tools/spawn_agent.py`\n- `handlers/agent/tools/tool_registry.py`\n\n**Analyzer Handler:**\n- `handlers/analyzer/analyzer_html_generator.py`\n- `handlers/analyzer/analyzer_json_exporter.py`\n- `handlers/analyzer/analyzer_models.py`\n- `handlers/analyzer/vad_eou_analyzer.py`\n\n**ASR Handlers:**\n- `handlers/asr/bailian_asr/asr_handler_bailian.py`\n- `handlers/asr/bailian_asr/pyproject.toml`\n- `handlers/asr/sensevoice/asr_handler_sensevoice.py`\n- `handlers/asr/sensevoice/pyproject.toml`\n\n**Avatar: FlashHead:**\n- `handlers/avatar/flashhead/avatar_handler_flashhead.py`\n- `handlers/avatar/flashhead/flashhead_config.py`\n- `handlers/avatar/flashhead/flashhead_face_crop.py`\n- `handlers/avatar/flashhead/flashhead_processor.py`\n- `handlers/avatar/flashhead/pyproject.toml`\n\n**Avatar: LAM:**\n- `handlers/avatar/lam/avatar_handler_lam_audio2expression.py`\n- `handlers/avatar/lam/pyproject.toml`\n- `handlers/avatar/lam/assets/arkit_face_channels.txt`\n\n**Avatar: LiteAvatar:**\n- `handlers/avatar/liteavatar/avatar_handler_liteavatar.py`\n- `handlers/avatar/liteavatar/avatar_output_handler.py`\n- `handlers/avatar/liteavatar/avatar_processor.py`\n- `handlers/avatar/liteavatar/avatar_processor_factory.py`\n- `handlers/avatar/liteavatar/liteavatar_handler_context.py`\n- `handlers/avatar/liteavatar/liteavatar_worker.py`\n- `handlers/avatar/liteavatar/liteavatar_worker_manager.py`\n- `handlers/avatar/liteavatar/pyproject.toml`\n- `handlers/avatar/liteavatar/shared_memory_buffer_pool.py`\n\n**LiteAvatar Algorithm:**\n- `handlers/avatar/liteavatar/algo/audio2signal_speed_limiter.py`\n- `handlers/avatar/liteavatar/algo/base_algo_adapter.py`\n- `handlers/avatar/liteavatar/algo/bg_frame_counter.py`\n- `handlers/avatar/liteavatar/algo/tts2face_cpu_adapter.py`\n\n**LiteAvatar Media:**\n- `handlers/avatar/liteavatar/media/speech_audio_aligner.py`\n- `handlers/avatar/liteavatar/media/speech_audio_processor.py`\n- `handlers/avatar/liteavatar/media/video_audio_aligner.py`\n\n**LiteAvatar Model:**\n- `handlers/avatar/liteavatar/model/algo_model.py`\n- `handlers/avatar/liteavatar/model/audio_input.py`\n\n**Avatar: MuseTalk:**\n- `handlers/avatar/musetalk/ARCHITECTURE.md`\n- `handlers/avatar/musetalk/avatar_handler_musetalk.py`\n- `handlers/avatar/musetalk/musetalk_algo.py`\n- `handlers/avatar/musetalk/musetalk_config.py`\n- `handlers/avatar/musetalk/musetalk_data_models.py`\n- `handlers/avatar/musetalk/musetalk_processor.py`\n- `handlers/avatar/musetalk/musetalk_review.md`\n- `handlers/avatar/musetalk/musetalk_utils_preprocessing.py`\n- `handlers/avatar/musetalk/pyproject.toml`\n\n**Avatar: Without Avatar:**\n- (placeholder directory structure)\n\n**GitHub Workflows:**\n- `.github/workflows/deploy-docs.yml`\n\nThis repository represents an extensive avatar chat system with modular handlers for speech recognition, text-to-speech, avatar animation, and AI agent functionalities."}]},"uuid":"4256904a-fc8e-4f44-924d-a8f31c187c26","timestamp":"2026-06-15T21:07:17.074Z","sourceToolAssistantUUID":"6a0de276-c235-4c49-8485-cbf957a1b084","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"4256904a-fc8e-4f44-924d-a8f31c187c26","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01GYaR2YNqgiU7bUz8uKM71Q","type":"message","role":"assistant","content":[{"type":"text","text":"Now I have the key file: `handlers/avatar/lam/avatar_handler_lam_audio2expression.py`. Let me fetch it."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":5367,"cache_read_input_tokens":51563,"cache_creation":{"ephemeral_5m_input_tokens":5367,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5jAGDARH987Wj4CWSGd","attributionAgent":"general-purpose","type":"assistant","uuid":"e32a5393-e978-46a5-996e-75920550c302","timestamp":"2026-06-15T21:07:20.306Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"4351a0a7-f811-4f8a-8249-c12ab84fae32","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01GYaR2YNqgiU7bUz8uKM71Q","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014CwS1YXM8jq1e9vqyDGjBL","name":"WebFetch","input":{"url":"https://raw.githubusercontent.com/aigc3d/LAM_Audio2Expression/main/inference_streaming_audio.py","prompt":"Show the COMPLETE Python source code - all imports, class definitions, functions. How does streaming audio input work? What format does audio come in? What is the output?"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":5367,"cache_read_input_tokens":51563,"output_tokens":285,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":5367},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":285,"cache_read_input_tokens":51563,"cache_creation_input_tokens":5367,"cache_creation":{"ephemeral_5m_input_tokens":5367,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5jAGDARH987Wj4CWSGd","attributionAgent":"general-purpose","type":"assistant","uuid":"1af1721e-69d8-4742-afde-34217deb5bfa","timestamp":"2026-06-15T21:07:23.516Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"1af1721e-69d8-4742-afde-34217deb5bfa","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014CwS1YXM8jq1e9vqyDGjBL","type":"tool_result","content":"# Audio Streaming Analysis\n\n## Complete Source Code\nThe provided content shows a **main inference script** (not a complete library). Key components:\n\n```python\nfrom engines.defaults import default_argument_parser, default_config_parser, default_setup\nfrom engines.infer import INFER\nimport librosa\nfrom tqdm import tqdm\nimport time\nfrom models.utils import export_blendshape_animation, ARKitBlendShape\n```\n\nThe script includes one custom function:\n- `export_json()` - exports blend shape animation data to JSON format at 30fps using ARKit format\n\n## Streaming Audio Input Mechanism\n\n**Audio Loading & Processing:**\n- Audio is loaded via `librosa.load()` at 16kHz sample rate\n- The stream is divided into **1-second chunks** (16,000 samples at 16kHz)\n- Processing: `audio[i*gap:(i+1)*gap]` where `gap = 16000`\n\n**Streaming Context:**\nThe function `infer_streaming_audio()` maintains a persistent `context` variable between calls, enabling the model to process audio sequentially while preserving state information.\n\n## Input/Output Format\n\n**Input:** Raw audio array (NumPy format) at 16kHz mono\n\n**Output:** Dictionary containing `'expression'` key with facial expression/blend shape data (NumPy array)\n\n**Final Output:** All expressions concatenated into a single array, then exported as JSON representing \"ARKit blend shape animation\" at 30fps—a format for facial animation data used in 3D character generation."}]},"uuid":"950f1b34-2165-45df-b2a2-ebbbad2f5a85","timestamp":"2026-06-15T21:07:28.295Z","sourceToolAssistantUUID":"1af1721e-69d8-4742-afde-34217deb5bfa","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"856bdc4b-81da-457c-97ea-50473b127f2b","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01282r6WBGDZxkedraWeWkk2","type":"tool_result","content":"# Full Raw Source Code Line by Line\n\n```\n1  # Copyright 2024-2025 The Alibaba 3DAIGC Team Authors. All rights reserved.\n2  \n3  Licensed under the Apache License, Version 2.0 (the \"License\");\n4  you may not use this file except in compliance with the License.\n5  You may obtain a copy of the License at\n6  \n7      https://www.apache.org/licenses/LICENSE-2.0\n8  \n9  Unless required by applicable law or agreed to in writing, software\n10 distributed under the License is distributed on an \"AS IS\" BASIS,\n11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n12 See the License for the specific language governing permissions and\n13 limitations under the License.\n14\n15 \"\"\"\n16\n17 import numpy as np\n18\n19 from engines.defaults import (\n20     default_argument_parser,\n21     default_config_parser,\n21     default_setup,\n22 )\n23 from engines.infer import INFER\n24 import librosa\n25 from tqdm import tqdm\n26 import time\n27\n28\n29 def export_json(bs_array, json_path):\n30     from models.utils import export_blendshape_animation, ARKitBlendShape\n31     export_blendshape_animation(bs_array, json_path, ARKitBlendShape, fps=30.0)\n32\n33 if __name__ == '__main__':\n34     args = default_argument_parser().parse_args()\n35     args.config_file = 'configs/lam_audio2exp_config_streaming.py'\n36     cfg = default_config_parser(args.config_file, args.options)\n37\n38\n39     cfg = default_setup(cfg)\n40     infer = INFER.build(dict(type=cfg.infer.type, cfg=cfg))\n41     infer.model.eval()\n42\n43     audio, sample_rate = librosa.load(cfg.audio_input, sr=16000)\n44     context = None\n45     input_num = audio.shape[0]//16000+1\n46     gap = 16000\n47     all_exp = []\n48     for i in tqdm(range(input_num)):\n49\n50         start = time.time()\n51         output, context = infer.infer_streaming_audio(audio[i*gap:(i+1)*gap], sample_rate, context)\n52         end = time.time()\n53         print('Inference time {}'.format(end - start))\n54         all_exp.append(output['expression'])\n55\n56     all_exp = np.concatenate(all_exp,axis=0)\n57\n58     export_json(all_exp, cfg.save_json_path)\n```"}]},"uuid":"f053b125-69d6-4439-ae4d-aba86407f677","timestamp":"2026-06-15T21:07:34.737Z","sourceToolAssistantUUID":"856bdc4b-81da-457c-97ea-50473b127f2b","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"df3af931-898f-4c53-98fc-ce93e1634853","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UnFDafAjZNnqZzcH9sS9Cf","type":"tool_result","content":"# Audio-to-Expression Neural Network Module\n\nThis codebase implements multiple neural architectures for audio-driven facial expression generation. Here's a comprehensive overview:\n\n## Core Architecture Classes\n\n### 1. **Audio2Expression** (Main Model)\n- **Input**: Dictionary containing `input_audio_array` (B×T), `id_idx`, optional `time_steps`\n- **Output**: Expression parameters via sigmoid, shape (B×T×52)\n- **Key components**:\n  - Audio encoder (Wav2Vec2 or WavLM, outputs 768-dim features)\n  - Feature projection to hidden dimension (512-dim default)\n  - Identity encoder for conditional processing\n  - Decoder stack with ConvNormRelu blocks\n  - Linear projection to expression_dim (52 default)\n\n### 2. **AudioIdentityEncoder**\n- **Input**: Audio features (B×hidden_dim×T), identity tensor\n- **Output**: Refined features (B×hidden_dim×T)\n- **Process**: \"Concatenates identity embeddings with audio features, processes through sequential layers with optional transformer\"\n\n### 3. **MeshtalkEncoder**\n- **Input**: Audio tensor (B×T×16000)\n- **Output**: Latent embedding dictionary with \"code\" key (B×T×latent_dim)\n- **Architecture**: MelSpectrogram → Conv1d layers with dilations → latent projection\n\n### 4. **GeneratorTransformer**\n- **Input**: Spectrogram, optional ground-truth poses, identity\n- **Output**: Pose predictions (B×T×32)\n- **Features**: Dual audio encoding pathways (MeshTalk/FaceFormer options), transformer decoder with periodic positional encoding\n\n### 5. **UniTalkerDecoderTransformer**\n- **Input**: Hidden states (B×T×out_dim), style index, frame count\n- **Output**: Decorated features (B×T×out_dim)\n- **Distinctive element**: \"Learnable style embeddings with biased attention masking and periodic positional encoding\"\n\n## Utility Components\n\n- **ConvNormRelu**: Flexible convolution block supporting 1D/2D, batch/group/layer normalization, residual connections\n- **SeqTranslator1D**: Sequential 1D convolution translator\n- **PeriodicPositionalEncoding**: \"Sine/cosine positional encoding with periodic structure for temporal sequences\"\n- **audio_chunking()**: Slices 16kHz audio into frame-aligned chunks\n\nAll models operate on 16kHz audio sampled at ~30 fps video frame rate."}]},"uuid":"5cf8a703-71c5-4710-b43e-fe6e784feca5","timestamp":"2026-06-15T21:07:39.824Z","sourceToolAssistantUUID":"df3af931-898f-4c53-98fc-ce93e1634853","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"12d7cd8c-d6d6-4494-84c8-652e9586507c","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017HLQugh3cK4R9QFM8oyT4k","type":"tool_result","content":"# ARKit Blendshape Names and Export Function\n\n## The 52 ARKit Blendshape Names\n\nThe code defines these as a Python list constant:\n\n```python\nARKitBlendShape =[\n   \"browDownLeft\", \"browDownRight\", \"browInnerUp\", \"browOuterUpLeft\",\n   \"browOuterUpRight\", \"cheekPuff\", \"cheekSquintLeft\", \"cheekSquintRight\",\n   \"eyeBlinkLeft\", \"eyeBlinkRight\", \"eyeLookDownLeft\", \"eyeLookDownRight\",\n   \"eyeLookInLeft\", \"eyeLookInRight\", \"eyeLookOutLeft\", \"eyeLookOutRight\",\n   \"eyeLookUpLeft\", \"eyeLookUpRight\", \"eyeSquintLeft\", \"eyeSquintRight\",\n   \"eyeWideLeft\", \"eyeWideRight\", \"jawForward\", \"jawLeft\", \"jawOpen\",\n   \"jawRight\", \"mouthClose\", \"mouthDimpleLeft\", \"mouthDimpleRight\",\n   \"mouthFrownLeft\", \"mouthFrownRight\", \"mouthFunnel\", \"mouthLeft\",\n   \"mouthLowerDownLeft\", \"mouthLowerDownRight\", \"mouthPressLeft\",\n   \"mouthPressRight\", \"mouthPucker\", \"mouthRight\", \"mouthRollLower\",\n   \"mouthRollUpper\", \"mouthShrugLower\", \"mouthShrugUpper\", \"mouthSmileLeft\",\n   \"mouthSmileRight\", \"mouthStretchLeft\", \"mouthStretchRight\",\n   \"mouthUpperUpLeft\", \"mouthUpperUpRight\", \"noseSneerLeft\", \"noseSneerRight\",\n   \"tongueOut\"\n]\n```\n\n## Export Function Overview\n\nThe `export_blendshape_animation()` function serializes animation data to JSON. It accepts \"52 blendshape weights (N frames), output path, corresponding name labels, frame rate,\" and optional rotation data. The function validates inputs, constructs a structured dictionary with metadata and per-frame weights with timing, and writes to a JSON file with error handling."}]},"uuid":"dfdc134a-3bbe-4a8f-8a6b-4c7f7778dbf7","timestamp":"2026-06-15T21:07:48.326Z","sourceToolAssistantUUID":"12d7cd8c-d6d6-4494-84c8-652e9586507c","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"9d355ad5-0dfc-4c58-971f-6522ea5e88a0","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MQcfUkdrzSa75k6JzD7TdA","type":"tool_result","content":"# LAM Quick Start Documentation Summary\n\nBased on the GitHub documentation provided, here's what the LAM setup entails:\n\n## Overview\nThe LAM integration enables \"端侧渲染\" (edge-side rendering) using Gaussian Splatting assets. The system leverages CosyVoice from Bailian for speech, with only VAD (Voice Activity Detection) and ASR (Automatic Speech Recognition) running locally on GPU, keeping hardware demands minimal.\n\n## Handler Components\n\nThe pipeline uses six specialized handlers:\n\n1. **Client**: WebSocket LAM client for communication\n2. **VAD**: Silero-based voice detection\n3. **ASR**: SenseVoice for speech recognition\n4. **LLM**: OpenAI-compatible language model interface\n5. **TTS**: Bailian CosyVoice for speech synthesis\n6. **Avatar**: LAM audio-to-expression conversion\n\n## Setup Process\n\nThe implementation requires three sequential steps:\n\n- Installing dependencies via configuration file\n- Downloading necessary models for the LAM handler\n- Launching the demo application\n\nEach handler links to detailed reference documentation for configuration specifics. The architecture supports \"一机多路\" (multiple streams per machine), suggesting scalability for concurrent interactions."}]},"uuid":"da2ecd2a-9039-49b7-ab4d-e90d953a78c6","timestamp":"2026-06-15T21:08:24.645Z","sourceToolAssistantUUID":"9d355ad5-0dfc-4c58-971f-6522ea5e88a0","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"699a8f2d-8fcd-425f-b831-7e2b74f0eb20","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01WHSU768kEaVjDCmmhZAiwX","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JfWvraFKbPN5HKrVQa4GA2","name":"WebFetch","input":{"url":"https://raw.githubusercontent.com/HumanAIGC-Engineering/OpenAvatarChat/main/docs/guide/how-it-works.md","prompt":"Show the complete content about how the pipeline works"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":611,"cache_read_input_tokens":62608,"output_tokens":206,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":611},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":206,"cache_read_input_tokens":62608,"cache_creation_input_tokens":611,"cache_creation":{"ephemeral_5m_input_tokens":611,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5jFFEWqyijza7nmhW34","attributionAgent":"general-purpose","type":"assistant","uuid":"2e8d8ce1-8de5-44cc-8d22-b0ab181c139a","timestamp":"2026-06-15T21:08:28.536Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"699a8f2d-8fcd-425f-b831-7e2b74f0eb20","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01F8SMkwhPQ86PFjY9Nu1Mch","type":"tool_result","content":"# LAM Quick Start Guide Summary\n\nThis documentation covers setting up LAM, a Gaussian Splatting asset renderer with voice capabilities. Here are the key points:\n\n## Core Components\n\nThe system uses several integrated handlers: a WebSocket client for LAM, Silero for voice activity detection, SenseVoice for speech recognition, OpenAI-compatible LLM support, Bailian's CosyVoice for text-to-speech, and LAM's audio-to-expression avatar handler.\n\n## Key Features\n\n\"语音使用百炼上的 CosyVoice，只有 VAD 和 ASR 运行在本地 GPU\" — the setup relies on cloud-based voice synthesis while keeping voice processing local, making it lightweight for multi-channel deployment.\n\n## Setup Process\n\nGetting started involves three straightforward steps: installing dependencies via a configuration file, downloading necessary models for LAM, and launching the demo application. All commands use the `uv` package manager with specific configuration and script files.\n\nThis approach balances local processing efficiency with cloud services for voice generation, suitable for resource-constrained environments requiring multiple concurrent streams."}]},"uuid":"05d40f0b-b0c2-4ada-8ec3-dcad0e7a9251","timestamp":"2026-06-15T21:08:31.982Z","sourceToolAssistantUUID":"699a8f2d-8fcd-425f-b831-7e2b74f0eb20","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"2e8d8ce1-8de5-44cc-8d22-b0ab181c139a","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JfWvraFKbPN5HKrVQa4GA2","type":"tool_result","content":"# Open Avatar Chat Pipeline Overview\n\n## Core Architecture\n\nThe system employs \"a modular Handler architecture\" that combines different components. The main modules include:\n\n- **Client Handler**: Manages WebRTC audio/video streaming\n- **VAD Handler**: Detects when users are speaking\n- **ASR Handler**: Converts speech to text\n- **LLM Handler**: Generates conversational responses\n- **Agent Handler**: Handles multi-turn tool interactions (Beta feature)\n- **TTS Handler**: Synthesizes text into speech\n- **Avatar Handler**: Creates facial animations synchronized with audio\n\n## Processing Flow\n\nThe interaction sequence follows these steps:\n\n1. Browser transmits audio/video via WebRTC\n2. Voice activity detection identifies speech\n3. Speech recognition converts audio to text\n4. Language model or agent generates responses\n5. Text-to-speech creates audio output\n6. Avatar engine produces matching facial movements\n7. Synthesized media returns through WebRTC\n\n## Performance Metrics\n\nTesting on high-end hardware (i9-13900KF + RTX 4090) showed \"average latency of approximately 2.2 seconds.\" This measurement spans from when users finish speaking until the avatar begins responding, encompassing network transmission delays, VAD processing time, and computational overhead throughout the entire pipeline."}]},"uuid":"b1100337-557d-4a01-9a65-56006cbced31","timestamp":"2026-06-15T21:08:32.185Z","sourceToolAssistantUUID":"2e8d8ce1-8de5-44cc-8d22-b0ab181c139a","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"b1100337-557d-4a01-9a65-56006cbced31","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01QmEpeUYfKmSXoM86ouvXiZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014xAUHoCUYYvUBDZ2yHerGC","name":"WebFetch","input":{"url":"https://raw.githubusercontent.com/HumanAIGC-Engineering/OpenAvatarChat/main/docs/reference/handlers/client/lam-client.md","prompt":"Show the complete documentation about the LAM client - what protocol it uses, how it communicates, WebSocket details, data format"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":805,"cache_read_input_tokens":63219,"cache_creation":{"ephemeral_5m_input_tokens":805,"ephemeral_1h_input_tokens":0},"output_tokens":61,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5jFp2jPSiyi9U4mhsse","attributionAgent":"general-purpose","type":"assistant","uuid":"b45191d2-d99b-4e69-92af-e4c5cc24cf90","timestamp":"2026-06-15T21:08:35.474Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"b45191d2-d99b-4e69-92af-e4c5cc24cf90","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01QmEpeUYfKmSXoM86ouvXiZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01EiTkKiKCfRRwDe2wTP1eD3","name":"WebFetch","input":{"url":"https://raw.githubusercontent.com/HumanAIGC-Engineering/OpenAvatarChat/main/docs/reference/handlers/avatar/lam.md","prompt":"Show the complete documentation about the LAM avatar handler"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":805,"cache_read_input_tokens":63219,"output_tokens":233,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":805},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":233,"cache_read_input_tokens":63219,"cache_creation_input_tokens":805,"cache_creation":{"ephemeral_5m_input_tokens":805,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5jFp2jPSiyi9U4mhsse","attributionAgent":"general-purpose","type":"assistant","uuid":"27ce2452-69ee-40f2-b4d3-75d369ee5437","timestamp":"2026-06-15T21:08:36.075Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"b45191d2-d99b-4e69-92af-e4c5cc24cf90","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014xAUHoCUYYvUBDZ2yHerGC","type":"tool_result","content":"# LAM Client Documentation Summary\n\nBased on the provided content, here's what the documentation reveals about the LAM client:\n\n## Protocol & Communication\nThe LAM client handler extends the \"RTC Client Handler\" and operates as a WebSocket-based solution. The module reference indicates it uses `ws_lam_client_handler`, suggesting WebSocket as the underlying communication protocol.\n\n## Key Capabilities\nThe system supports multiple simultaneous connections through a configurable concurrency limit. The implementation allows selection from pre-trained character models for rendering purposes.\n\n## Configuration\nUsers can specify character assets via configuration file:\n- **Module path**: `client/ws_lam_client/ws_lam_client_handler`\n- **Asset format**: ZIP files containing model data\n- **Example asset**: `lam_samples/barbara.zip`\n- **Concurrency control**: Adjustable connection limits\n\n## Limitations of Available Information\nThe provided documentation excerpt doesn't include specific details about:\n- Exact WebSocket message formats or data structures\n- Protocol handshake procedures\n- Payload specifications for client-server communication\n- Detailed architectural diagrams\n\nFor comprehensive protocol specifications and data format details, you would need to consult the full documentation or the LAM project repository directly."}]},"uuid":"3e984e3f-4d1b-42a8-b7c3-d9cc77f70a79","timestamp":"2026-06-15T21:08:39.520Z","sourceToolAssistantUUID":"b45191d2-d99b-4e69-92af-e4c5cc24cf90","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"27ce2452-69ee-40f2-b4d3-75d369ee5437","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EiTkKiKCfRRwDe2wTP1eD3","type":"tool_result","content":"# LAM Avatar Handler Documentation\n\n## Overview\nThe LAM digital avatar handler enables audio-driven character animation capabilities.\n\n## Model Dependencies\n\nThe handler requires two key models:\n\n**1. wav2vec2-base-960h**\nThis speech recognition model can be downloaded from HuggingFace or ModelScope (recommended for users in mainland China). Users may clone the repository directly using git commands provided in the documentation.\n\n**2. LAM_audio2exp**\nThis model converts audio to facial expressions. It's available through HuggingFace or alternatively via Alibaba Cloud OSS for improved download speeds in certain regions.\n\n## Installation Methods\n\n**Automatic Setup:**\nRun the download script specifying the LAM handler. Mainland Chinese users should add the ModelScope source parameter for faster downloads.\n\n**Manual Installation:**\nDetailed instructions are provided for both models, including git clone commands for the speech model and wget/tar extraction commands for the audio-to-expression model from multiple mirror sources.\n\nThe documentation emphasizes accessibility for international users while providing optimized download options for specific regions, making deployment flexible across different network environments."}]},"uuid":"78ecb1a5-db66-4834-bd66-3a393a4fc11a","timestamp":"2026-06-15T21:08:40.226Z","sourceToolAssistantUUID":"27ce2452-69ee-40f2-b4d3-75d369ee5437","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"78ecb1a5-db66-4834-bd66-3a393a4fc11a","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01RjdjpeGjRzdJG4UXX1aemG","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TDYFWEitF2boYL7c9N3BCG","name":"WebFetch","input":{"url":"https://raw.githubusercontent.com/HumanAIGC-Engineering/OpenAvatarChat/main/config/chat_with_lam.yaml","prompt":"Show the complete YAML configuration for LAM chat mode"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":808,"cache_read_input_tokens":64024,"cache_creation":{"ephemeral_5m_input_tokens":808,"ephemeral_1h_input_tokens":0},"output_tokens":59,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5jGPmVzfzZ6bjm4gGqX","attributionAgent":"general-purpose","type":"assistant","uuid":"20d41ffe-68ba-4641-8fab-7ad904645582","timestamp":"2026-06-15T21:08:43.736Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"20d41ffe-68ba-4641-8fab-7ad904645582","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TDYFWEitF2boYL7c9N3BCG","type":"tool_result","content":"# LAM Chat Mode YAML Configuration\n\nBased on the provided configuration, here's the complete setup for LAM (Live Avatar Mode) chat:\n\n## Core Components\n\n**Service Layer:**\n- Host: `0.0.0.0`, Port: `8282`\n- SSL certificates configured for secure connections\n\n**Chat Engine:**\n- Model directory: `models`\n- Maximum concurrent sessions: `5`\n\n## Handler Configuration\n\nThe LAM setup includes these key handlers:\n\n1. **LamClient** - Manages WebSocket connections with `connection_ttl: 900` seconds\n2. **SileroVad** - Voice activity detection with configurable thresholds\n3. **SenseVoice** - ASR module for speech recognition\n4. **CosyVoice** - Text-to-speech synthesis (voice: \"longxiaocheng\")\n5. **LLMOpenAICompatible** - Language model integration using Qwen-Plus\n6. **LAM_Driver** - Avatar handler for audio-to-expression conversion\n\n## System Behavior\n\nThe system is configured to produce \"brief two-to-three sentence responses with appropriate punctuation,\" operating as an interactive AI assistant through the LAM avatar framework with real-time speech processing and synthesis capabilities."}]},"uuid":"918996f7-b809-4a0a-b846-ce0466157f23","timestamp":"2026-06-15T21:08:47.300Z","sourceToolAssistantUUID":"20d41ffe-68ba-4641-8fab-7ad904645582","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"918996f7-b809-4a0a-b846-ce0466157f23","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01RjdjpeGjRzdJG4UXX1aemG","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HBCTr9RAhDFGxFjbApLQ9A","name":"WebFetch","input":{"url":"https://github.com/aigc3d/LAM_WebRender","prompt":"Extract all content about the WebGL renderer - what protocol it uses to receive avatar data, how it renders Gaussian splats in browser, WebSocket data format, JavaScript API"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":808,"cache_read_input_tokens":64024,"cache_creation":{"ephemeral_5m_input_tokens":808,"ephemeral_1h_input_tokens":0},"output_tokens":59,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5jGPmVzfzZ6bjm4gGqX","attributionAgent":"general-purpose","type":"assistant","uuid":"e85068da-fc3c-4543-968c-e716385db4c5","timestamp":"2026-06-15T21:08:48.462Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"e85068da-fc3c-4543-968c-e716385db4c5","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HBCTr9RAhDFGxFjbApLQ9A","type":"tool_result","content":"# LAM_WebRender: WebGL Renderer Analysis\n\nBased on the GitHub repository content provided, here's what's documented about the renderer:\n\n## Core Technology\nThe project describes itself as a **\"Lightweight WebGL Renderer\"** for real-time 3D avatar rendering. It's implemented in TypeScript (83.2% of codebase) with HTML components.\n\n## Data Integration\nThe renderer accepts avatar model data as ZIP files. The documentation shows: **\"./asset/arkit/p2-1.zip\"** as the example model format, and facial animation data via JSON files like **\"./asset/test_expression_1s.json\"** for expression control.\n\n## API Usage\nThe NPM package provides a straightforward JavaScript interface:\n\n```javascript\nconst render = await GaussianSplats3D.GaussianSplatRenderer.getInstance(div, assetPath);\n```\n\nThe renderer instantiates with a DOM element target and asset path pointing to model files.\n\n## Limitations in Available Documentation\nThe provided content does **not** detail:\n- Specific WebSocket protocols or real-time data formats\n- Internal Gaussian splatting rendering algorithms\n- Binary data structures for streaming\n- Network communication specifics\n\nThe repository appears focused on integration and usage rather than technical architecture documentation. For implementation details, consulting the actual source code in the `/src` directory or the [NPM package documentation](https://www.npmjs.com/package/gaussian-splat-renderer-for-lam) would be necessary."}]},"uuid":"a28d2279-59e9-4e3a-a1f3-f122df3719db","timestamp":"2026-06-15T21:08:55.361Z","sourceToolAssistantUUID":"e85068da-fc3c-4543-968c-e716385db4c5","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"42cbdf60-2517-4393-af4e-4f49d9b9c08e","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_012iVQmVutTWSdfa2MaRcy54","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SenxuLVto2nmeunqVGoK7S","name":"WebFetch","input":{"url":"https://api.github.com/repos/aigc3d/LAM_WebRender/git/trees/main?recursive=1","prompt":"List all file paths in the repository"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":896,"cache_read_input_tokens":64832,"output_tokens":187,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":896},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":187,"cache_read_input_tokens":64832,"cache_creation_input_tokens":896,"cache_creation":{"ephemeral_5m_input_tokens":896,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5jHWVqfJznzzjh2GwTQ","attributionAgent":"general-purpose","type":"assistant","uuid":"dfe2f347-1ae4-449c-b28f-0503c8f39887","timestamp":"2026-06-15T21:08:58.842Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"42cbdf60-2517-4393-af4e-4f49d9b9c08e","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SRR8CUdz9PPPU9h6uEdYaC","type":"tool_result","content":"# LAM_WebRender Overview\n\nLAM_WebRender is a lightweight WebGL renderer designed for real-time 3D Gaussian Splatting, specifically built to work with LAM avatars and facial animation systems.\n\n## Key Capabilities\n\nThe engine offers \"real-time rendering support: Interactive LAM avatars with LAM_Audio2Expression facial animations.\" It's distributed as an NPM package for easy integration into web projects.\n\n## Getting Started\n\nInstallation requires three simple steps: install dependencies via npm, launch the development server, and access it through a local browser connection at port 5173.\n\n## Core Functionality\n\nThe system integrates three complementary technologies:\n- **Avatar generation** via the LAM project\n- **Expression synthesis** through LAM_Audio2Expression (converting audio to ARKit blendshapes)\n- **Real-time visualization** using this rendering engine\n\n## Usage\n\nDevelopers can integrate the package by importing the renderer and specifying asset paths for avatar models (ZIP files) and expression data (JSON format).\n\n## Advanced Features\n\nThe documentation notes that \"custom avatars\" can be created by \"generating new models via LAM and replacing ZIP files,\" allowing for extensibility beyond pre-built examples.\n\nThe project operates under MIT License terms."}]},"uuid":"84832b82-269e-4137-97d4-3800f6b53650","timestamp":"2026-06-15T21:09:03.020Z","sourceToolAssistantUUID":"42cbdf60-2517-4393-af4e-4f49d9b9c08e","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"dfe2f347-1ae4-449c-b28f-0503c8f39887","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SenxuLVto2nmeunqVGoK7S","type":"tool_result","content":"# File Paths in LAM_WebRender Repository\n\nBased on the GitHub API response, here are all the files and directories:\n\n**Root Level Files:**\n- .gitignore\n- LICENSE\n- README.md\n- index.html\n- package.json\n- tsconfig.json\n- vite.config.ts\n\n**Directories:**\n- asset/\n- asset/arkit/\n- src/\n\n**Asset Files:**\n- asset/arkit/p2-1.zip\n- asset/test_expression_1s.json\n\n**Source Files:**\n- src/gaussianAvatar.ts\n- src/main.ts\n\nThis appears to be a web rendering project using TypeScript and Vite, with Gaussian avatar functionality and test assets included."}]},"uuid":"455cb6ad-73a4-48bc-a487-6ee0b35ce8b1","timestamp":"2026-06-15T21:09:03.572Z","sourceToolAssistantUUID":"dfe2f347-1ae4-449c-b28f-0503c8f39887","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"dd87cd06-f570-412e-96fb-ecc45e5038c5","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01GReZxyQWDjFtb4ZqmRn6et","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01VsZzthsdCeRytt5meHRFFs","name":"WebFetch","input":{"url":"https://raw.githubusercontent.com/aigc3d/LAM_WebRender/main/src/main.ts","prompt":"Show the COMPLETE TypeScript source code"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":791,"cache_read_input_tokens":65728,"output_tokens":192,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":791},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":192,"cache_read_input_tokens":65728,"cache_creation_input_tokens":791,"cache_creation":{"ephemeral_5m_input_tokens":791,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5jJ7bU4QXGgEm2Si4g7","attributionAgent":"general-purpose","type":"assistant","uuid":"937bcc91-d06e-4445-a7ac-6c6a4bb434b7","timestamp":"2026-06-15T21:09:06.614Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"937bcc91-d06e-4445-a7ac-6c6a4bb434b7","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VsZzthsdCeRytt5meHRFFs","type":"tool_result","content":"# TypeScript Source Code Analysis\n\nBased on the provided snippet, here's what the complete TypeScript implementation would likely include:\n\n```typescript\nimport { GaussianAvatar } from './gaussianAvatar';\n\nconst div = document.getElementById('LAM_WebRender');\nconst assetPath = './asset/arkit/p2-1.zip';\n\nconst gaussianAvatar = new GaussianAvatar(div, assetPath);\ngaussianAvatar.start();\n```\n\n## Key Components\n\nThe code demonstrates:\n\n1. **Import Statement**: References a `GaussianAvatar` class from a local module\n2. **DOM Selection**: Targets an element with ID \"LAM_WebRender\"\n3. **Asset Configuration**: Points to a compressed asset file (p2-1.zip)\n4. **Initialization**: Creates an instance and invokes the start method\n\n## Missing Context\n\nTo provide the **complete** TypeScript source code, I would need:\n\n- The `GaussianAvatar` class definition (constructor, methods, properties)\n- Type definitions for the parameters\n- Any additional configuration or error handling\n- The full module structure\n\nThe snippet shown represents only the instantiation layer. The actual implementation details of the `GaussianAvatar` class aren't visible in this excerpt."}]},"uuid":"1d04e8b8-ce23-47b1-8143-81514d4db7fc","timestamp":"2026-06-15T21:09:10.036Z","sourceToolAssistantUUID":"937bcc91-d06e-4445-a7ac-6c6a4bb434b7","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"dd87cd06-f570-412e-96fb-ecc45e5038c5","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01AT97QRYfhF3e3njZ4J1yP4","type":"tool_result","content":"# GaussianAvatar TypeScript Source Code\n\nThe provided code presents a complete TypeScript class for rendering a Gaussian Splat 3D avatar. Here's the full source:\n\n```typescript\nimport * as GaussianSplats3D from \"gaussian-splat-renderer-for-lam\"\nimport bsData from \"../asset/test_expression_1s.json\"\n\nexport class GaussianAvatar {\n  private _avatarDivEle: HTMLDivElement;\n  private _assetsPath = \"\";\n  public curState = \"Idle\";\n  private _renderer: GaussianSplats3D.GaussianSplatRenderer;\n  \n  constructor(container: HTMLDivElement, assetsPath: string) {\n    this._avatarDivEle = container;\n    this._assetsPath = assetsPath;\n    this._init();\n  }\n  \n  private _init() {\n    if (!this._avatarDivEle || !this._assetsPath) {\n      throw new Error(\"Lack of necessary initialization parameters\");\n    }\n  }\n\n  public start() {\n    this.render();\n  }\n\n  public async render() {\n    this._renderer = await GaussianSplats3D.GaussianSplatRenderer.getInstance(\n      this._avatarDivEle,\n      this._assetsPath,\n      {\n        getChatState: this.getChatState.bind(this),\n        getExpressionData: this.getArkitFaceFrame.bind(this),\n        backgroundColor: \"0xff0000\",\n        alpha: 0.2\n      },\n    );\n    this.startTime = performance.now() / 1000;\n    setTimeout(() => { this.curState = \"Listening\" }, 5000);\n    setTimeout(() => { this.curState = \"Thinking\" }, 6000);\n    setTimeout(() => { this.curState = \"Responding\" }, 10000);\n  }\n  \n  expressitionData: any;\n  startTime = 0\n  \n  public getChatState() {\n    return this.curState;\n  }\n  \n  public getArkitFaceFrame() {\n    const length = bsData[\"frames\"].length\n    const frameInfoInternal = 1.0 / 30.0;\n    const currentTime = performance.now() / 1000;\n    const calcDelta = (currentTime - this.startTime) % (length * frameInfoInternal);\n    const frameIndex = Math.floor(calcDelta / frameInfoInternal)\n    this.expressitionData = {};\n\n    bsData[\"names\"].forEach((name: string, index: number) => {\n      this.expressitionData[name] = bsData[\"frames\"][frameIndex][\"weights\"][index]\n    })\n    return this.expressitionData;\n  }\n}\n```\n\n**Key functionality:** The class manages a 3D avatar renderer with state transitions (Idle → Listening → Thinking → Responding) and facial expression data mapped from frame-based animation data."}]},"uuid":"95672286-5e78-4978-9ae1-38c4d46302cf","timestamp":"2026-06-15T21:09:10.569Z","sourceToolAssistantUUID":"dd87cd06-f570-412e-96fb-ecc45e5038c5","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"ec7b293f-a2fa-4e99-b7a8-a84eaa6e83ee","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0178mNVSCPWf3HY9adSEAMmD","type":"tool_result","content":"The server returned HTTP 404 Not Found.\n\nThe response body was not retrieved. If this URL requires authentication, use an authenticated tool (e.g. `gh` for GitHub, or an MCP-provided fetch tool) instead of WebFetch."}]},"uuid":"ac51dc3a-6f5f-444f-8d16-727a2cb5d1e4","timestamp":"2026-06-15T21:09:15.506Z","sourceToolAssistantUUID":"ec7b293f-a2fa-4e99-b7a8-a84eaa6e83ee","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"9fd62d70-5a15-45ac-88bd-4026acd48696","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EjmZZmczY9Vp2e3acimoLq","type":"tool_result","content":"# JSON Structure Analysis\n\n## Top-Level Keys\nThe JSON object contains three primary keys:\n\n1. **`names`** - Array of facial expression/animation target names\n2. **`frames`** - Array of animation keyframes\n3. (Implied structure suggests this is animation data)\n\n## Names Array\nContains 52 facial blend shape identifiers controlling facial features:\n\n- **Brow controls**: browDownLeft, browDownRight, browInnerUp, browOuterUpLeft, browOuterUpRight\n- **Cheek controls**: cheekPuff, cheekSquintLeft, cheekSquintRight\n- **Eye controls**: eyeBlinkLeft, eyeBlinkRight, eyeLookDownLeft/Right, eyeLookInLeft/Right, eyeLookOutLeft/Right, eyeLookUpLeft/Right, eyeSquintLeft/Right, eyeWideLeft/Right\n- **Jaw controls**: jawForward, jawLeft, jawOpen, jawRight\n- **Mouth controls**: mouthClose, mouthDimpleLeft/Right, mouthFrownLeft/Right, mouthFunnel, mouthLeft/Right, mouthLowerDownLeft/Right, mouthPressLeft/Right, mouthPucker, mouthRollLower/Upper, mouthShrugLower/Upper, mouthSmileLeft/Right, mouthStretchLeft/Right, mouthUpperUpLeft/Right\n- **Nose controls**: noseSneerLeft, noseSneerRight\n- **Tongue**: tongueOut\n\n## Frame Structure\nEach frame object contains:\n\n- **`weights`** - Array of 52 numeric values (0.0–1.0 range) corresponding to each name, representing blend shape intensities\n- **`time`** - Float timestamp indicating frame position in animation sequence\n- **`rotation`** - Empty array (currently unused in provided data)\n\nThis represents facial animation keyframe data, likely for character rigging systems like those used in 3D graphics or game engines."}]},"uuid":"9eafc3f5-ca65-4145-9214-2deb04eae325","timestamp":"2026-06-15T21:09:21.177Z","sourceToolAssistantUUID":"9fd62d70-5a15-45ac-88bd-4026acd48696","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"a1963f74-e5ce-4a83-bf8e-0d39580974d1","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Bv4x813SxVYAF2aFmnMJnf","type":"tool_result","content":"# Audio2ExpressionInfer Class Overview\n\nThe provided code shows the complete implementation of the `Audio2ExpressionInfer` class, which is registered via the `@INFER.register_module()` decorator. Here's a summary of key components:\n\n## Main Inference Method\n\nThe `infer()` method processes audio input to generate facial expression parameters. It:\n- Loads audio at 16kHz sample rate using librosa\n- Optionally extracts vocal tracks using Spleeter\n- Passes audio through the model with identity encoding\n- Applies post-processing including \"smooth_mouth_movements\" and \"apply_random_brow_movement\"\n\n## Streaming Audio Processing\n\nThe `infer_streaming_audio()` method handles real-time audio input with context management:\n\n**Key features:**\n- Maintains a rolling context buffer with \"max_frame_length\" of 64 frames\n- Resamples incoming audio to match the model's expected sample rate\n- Concatenates previous audio segments with new input for temporal coherence\n- Returns expression parameters along with an updated context dictionary\n\n**Processing pipeline:**\n```\nAudio resampling → Audio concatenation → Model inference → \nExpression post-processing → Context state update\n```\n\n## Post-Processing Pipeline\n\nThe `apply_expression_postprocessing()` method executes sequential transformations:\n- \"smooth_mouth_movements\" for audio synchronization\n- \"apply_frame_blending\" for temporal smoothing\n- Savitzky-Golay filtering for noise reduction\n- \"symmetrize_blendshapes\" for facial symmetry\n- \"apply_random_eye_blinks_context\" for natural eye movement\n\nThe code is licensed under Apache License 2.0 by Alibaba's 3DAIGC Team."}]},"uuid":"e2546ba5-b5c5-462d-99eb-23fb58e2751e","timestamp":"2026-06-15T21:09:36.239Z","sourceToolAssistantUUID":"a1963f74-e5ce-4a83-bf8e-0d39580974d1","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"e2546ba5-b5c5-462d-99eb-23fb58e2751e","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_01AE8hksrkGUxRFbaJCW289u","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019QWv4XFaSNg1d9JR7gUq67","name":"WebFetch","input":{"url":"https://raw.githubusercontent.com/aigc3d/LAM_Audio2Expression/main/engines/infer.py","prompt":"Show the COMPLETE raw Python source code. Show every line including the infer_streaming_audio signature with parameter types, context structure, return type, and what model.forward() receives"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":743,"cache_read_input_tokens":68970,"cache_creation":{"ephemeral_5m_input_tokens":743,"ephemeral_1h_input_tokens":0},"output_tokens":73,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Cc5jLXDq9BVbKXZHK53M2","attributionAgent":"general-purpose","type":"assistant","uuid":"9b60af6a-ebae-4176-9017-4e5bfa7146a4","timestamp":"2026-06-15T21:09:40.574Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"9b60af6a-ebae-4176-9017-4e5bfa7146a4","isSidechain":true,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","agentId":"a8905ecd71f2dd288","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019QWv4XFaSNg1d9JR7gUq67","type":"tool_result","content":"# Audio2ExpressionInfer: Complete Source Code Analysis\n\n## Method Signature with Types\n\n```python\ndef infer_streaming_audio(self,\n                         audio: np.ndarray,\n                         ssr: float,\n                         context: dict) -> tuple[dict, dict]:\n```\n\n## Context Structure\n\nThe context dictionary maintains streaming state:\n\n```python\ncontext = {\n    'is_initial_input': bool,\n    'previous_audio': np.ndarray | None,\n    'previous_expression': np.ndarray | None,\n    'previous_volume': np.ndarray | None,\n    'first_input_flag': bool\n}\n```\n\nDefault initialization uses `DEFAULT_CONTEXT.copy()`.\n\n## Model Forward Input\n\nThe model receives:\n\n```python\ninput_dict = {\n    'id_idx': torch.Tensor,  # One-hot encoded identity [1, num_identity_classes]\n    'input_audio_array': torch.Tensor  # Audio signal [1, audio_samples]\n}\n\noutput_dict = self.model(input_dict)\n# Contains: output_dict['pred_exp']  # [frames, 52] blendshape parameters\n```\n\n## Return Structure\n\n```python\nreturn {\n    \"code\": str,  # RETURN_CODE['SUCCESS']\n    \"expression\": np.ndarray,  # [frames, 52] processed expressions\n    \"headpose\": None\n}, output_context  # Updated context dict\n```\n\n## Processing Pipeline\n\nAudio undergoes resampling, concatenation with context history (max 64 frames), model inference, then \"full post-processing pipeline\" including mouth smoothing, frame blending, Savitzky-Golay filtering, blendshape symmetrization, and eye blink synthesis."}]},"uuid":"5751d278-17ae-49da-9aa8-f1cb08d768c3","timestamp":"2026-06-15T21:09:45.390Z","sourceToolAssistantUUID":"9b60af6a-ebae-4176-9017-4e5bfa7146a4","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"5751d278-17ae-49da-9aa8-f1cb08d768c3","isSidechain":true,"agentId":"a8905ecd71f2dd288","message":{"model":"claude-sonnet-4-6","id":"msg_011eNzxgGvaMhHtyZRBPkuX5","type":"message","role":"assistant","content":[{"type":"text","text":"I now have all the information needed for a comprehensive answer. Let me compile the full picture.\n\nHere is a comprehensive technical report on the LAM + OpenAvatarChat pipeline for real-time talking avatar integration.\n\n---\n\n## Pipeline Architecture Overview\n\nThe full stack has three distinct layers: **Audio2Expression** (Python, server-side), **LAM Gaussian Splatting Renderer** (either Python server-side render *or* WebGL client-side), and a **WebRTC/WebSocket frontend**.\n\n---\n\n## 1. Real-Time Audio-Driven Avatar Pipeline\n\nThe end-to-end flow in OpenAvatarChat (`HumanAIGC-Engineering/OpenAvatarChat`, not `aigc3d/OpenAvatarChat`) is:\n\n```\nBrowser audio (mic) \n  → WebRTC → Python server\n    → VAD (Silero) → ASR (SenseVoice) → LLM → TTS (CosyVoice)\n      → Audio bytes → LAM Audio2Expression → ARKit blendshapes [52 floats/frame @ 30fps]\n        → WebSocket to browser → gaussian-splat-renderer-for-lam (WebGL)\n          → Canvas render\n```\n\nFor your TTS→Avatar use case, the VAD/ASR/LLM modules are bypassed, and TTS audio feeds directly into Audio2Expression.\n\n---\n\n## 2. Audio2Expression Module (`aigc3d/LAM_Audio2Expression`)\n\n**Key repo:** `https://github.com/aigc3d/LAM_Audio2Expression`\n\n**Input format:** Raw NumPy float32 audio at **16 kHz mono**, fed in 1-second chunks (16,000 samples per call).\n\n**Python callable API:**\n```python\nfrom engines.defaults import default_argument_parser, default_config_parser, default_setup\nfrom engines.infer import INFER\n\ncfg = default_config_parser('configs/lam_audio2exp_config_streaming.py', options)\ncfg = default_setup(cfg)\ninfer = INFER.build(dict(type=cfg.infer.type, cfg=cfg))\ninfer.model.eval()\n\ncontext = None\n# For each 1-second audio chunk:\noutput, context = infer.infer_streaming_audio(audio_chunk_np, sample_rate=16000, context)\n# output['expression'] → np.ndarray shape [N_frames, 52]  (30fps ARKit blendshapes)\n```\n\n**Streaming context structure:**\n```python\ncontext = {\n    'is_initial_input': bool,\n    'previous_audio': np.ndarray | None,\n    'previous_expression': np.ndarray | None,\n    'previous_volume': np.ndarray | None,\n    'first_input_flag': bool\n}\n```\n\n**Output:** `[N_frames, 52]` NumPy array. Each frame = 52 ARKit blendshape weights (0.0–1.0). The 52 names are the standard Apple ARKit face channels: `browDownLeft`, `browDownRight`, `browInnerUp`, `cheekPuff`, `eyeBlinkLeft`, `jawOpen`, `mouthSmileLeft`, `tongueOut`, etc.\n\n**Required models:** `wav2vec2-base-960h` (audio encoder) + `LAM_audio2exp` weights (as `LAM_audio2exp.tar` — **note: as of early 2026 this tarball is not yet publicly released**, per open issue #91 in the LAM repo).\n\n---\n\n## 3. Browser Rendering: WebGL + WebSocket (not WebRTC for avatar frames)\n\n**Key repo:** `https://github.com/aigc3d/LAM_WebRender`\n\nThe browser rendering is done via the NPM package `gaussian-splat-renderer-for-lam`. It is **WebGL-based Gaussian Splatting**, not video streaming. The avatar geometry (Gaussian Splat model) is loaded once as a ZIP file; only the expression data streams per frame.\n\n**JavaScript API:**\n```typescript\nimport * as GaussianSplats3D from \"gaussian-splat-renderer-for-lam\";\n\nconst renderer = await GaussianSplats3D.GaussianSplatRenderer.getInstance(\n  divElement,          // HTMLDivElement canvas container\n  \"./avatar.zip\",      // Pre-exported LAM avatar (GLB+Gaussian data)\n  {\n    getChatState: () => \"Responding\",  // \"Idle\"|\"Listening\"|\"Thinking\"|\"Responding\"\n    getExpressionData: () => ({        // Called every frame; return dict of 52 blendshape name→value\n      jawOpen: 0.4,\n      mouthSmileLeft: 0.2,\n      // ...all 52 ARKit channels\n    }),\n    backgroundColor: \"0x000000\",\n    alpha: 0.0\n  }\n);\n```\n\nThe renderer **polls `getExpressionData()` each animation frame** — there is no WebSocket push from server to renderer. This means your Python backend feeds blendshapes to a JS bridge (your own WebSocket), and the JS side passes them into `getExpressionData()` on each rAF tick.\n\n**Avatar asset format:** A ZIP file containing the LAM-exported GLB (Gaussian Splat geometry baked from a source photo). Generated via `app_lam.py --blender_path /path/to/blender` with the \"Export ZIP for Chatting Avatar\" checkbox enabled. Requires Blender 4.0+ and the FBX SDK.\n\n---\n\n## 4. OpenAvatarChat WebRTC: for Audio I/O Only\n\n**The WebRTC layer** (`gradio-webrtc`) is used *only for browser microphone audio input and TTS audio output back to the browser* — not for streaming avatar frames. The avatar render happens entirely in WebGL client-side. The LAM client handler is a **WebSocket** (`ws_lam_client_handler`) that delivers blendshape data from Python to the browser's JavaScript renderer.\n\n---\n\n## 5. LAM Core Model: Python-Importable Inference\n\nThe LAM 3D Gaussian model itself (`lam/models/modeling_lam.py`) is callable programmatically:\n\n```python\nfrom lam.runners.infer.lam import LAMInferrer\n\ninferrer = LAMInferrer()\n# Takes: source image + pre-computed FLAME params (from motion sequences)\n# Returns: rendered RGB frames [Nv, H, W, 3]\n```\n\n**FLAME parameter keys** (`gs_renderer.py`):\n- `expr`: [B, Nv, 100] — expression coefficients  \n- `betas`: [B, 100] — shape (constant per identity)  \n- `rotation`, `neck_pose`, `jaw_pose`, `eyes_pose`, `translation` — pose per frame  \n- `teeth_bs` (optional) — teeth blendshapes\n\nHowever, for your use case you **do not need to call the LAM Gaussian renderer in Python at all** — the WebGL renderer does the rendering client-side from the pre-exported ZIP. The Python side only needs Audio2Expression.\n\n---\n\n## 6. Key Files to Study for Integration\n\n| File | Repo | Purpose |\n|---|---|---|\n| `engines/infer.py` | `aigc3d/LAM_Audio2Expression` | `infer_streaming_audio()` — core callable API |\n| `models/network.py` | `aigc3d/LAM_Audio2Expression` | `Audio2Expression` model (Wav2Vec2 → 52 blendshapes) |\n| `models/utils.py` | `aigc3d/LAM_Audio2Expression` | `ARKitBlendShape` list + `export_blendshape_animation()` |\n| `inference_streaming_audio.py` | `aigc3d/LAM_Audio2Expression` | Reference streaming usage |\n| `src/gaussianAvatar.ts` | `aigc3d/LAM_WebRender` | JS renderer API and expression data protocol |\n| `handlers/avatar/lam/avatar_handler_lam_audio2expression.py` | `HumanAIGC-Engineering/OpenAvatarChat` | How audio bytes → blendshapes → WebSocket works in production |\n| `config/chat_with_lam.yaml` | `HumanAIGC-Engineering/OpenAvatarChat` | Full handler stack configuration |\n| `app_lam.py` | `aigc3d/LAM` | Avatar export (ZIP) with Blender |\n\n---\n\n## 7. Integration Plan for Your Use Case\n\nYour pipeline (TTS audio → animated avatar in browser canvas):\n\n1. **Python backend**: Load `Audio2ExpressionInfer` once at startup. For each TTS response, convert audio to 16kHz numpy, call `infer_streaming_audio()` in 1-second chunks, buffer output blendshape frames.\n2. **WebSocket**: Stream blendshape frames as JSON `{jawOpen: 0.4, eyeBlinkLeft: 0.1, ...}` to the browser at 30fps.\n3. **Browser JS**: Load `gaussian-splat-renderer-for-lam` with the pre-exported avatar ZIP. Wire `getExpressionData()` to read from the WebSocket buffer. The renderer calls this each animation frame and deforms the Gaussian Splat mesh live in WebGL.\n\n**Critical blocker to verify:** The `LAM_audio2exp` model weights (`LAM_audio2exp.tar`) appear to not be publicly available as of February 2026 (GitHub issue #91 in `aigc3d/LAM` is open and unanswered). 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