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https://github.com/aigc3d/LAM_Audio2Expression.git
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104 lines
3.5 KiB
Markdown
104 lines
3.5 KiB
Markdown
# LAM-A2E: Audio to Expression
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[](https://aigc3d.github.io/projects/LAM/)
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[](https://www.apache.org/licenses/LICENSE-2.0)
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#### This project leverages audio input to generate ARKit blendshapes-driven facial expressions in ⚡real-time⚡, powering ultra-realistic 3D avatars generated by [LAM](https://github.com/aigc3d/LAM).
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## Demo
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<div align="center">
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<video controls src="https://github.com/user-attachments/assets/30ccbe82-7933-4031-8578-b5248435d317">
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</video>
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</div>
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## 📢 News
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### To do list
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- [ ] Release Huggingface space.
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- [ ] Release Modelscope space.
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- [ ] Release the LAM-A2E model based on the Flame expression.
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- [ ] Release Interactive Chatting Avatar SDK with [OpenAvatarChat](https://github.com/HumanAIGC-Engineering/OpenAvatarChat), including LLM, ASR, TTS, LAM-Avatars.
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## 🚀 Get Started
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### Environment Setup
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```bash
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git clone git@github.com:aigc3d/LAM_Audio2Expression.git
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cd LAM_Audio2Expression
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# Install with Cuda 12.1
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sh ./scripts/install/install_cu121.sh
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# Or Install with Cuda 11.8
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sh ./scripts/install/install_cu118.sh
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```
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### Download
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```
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# HuggingFace download
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# Download Assets and Model Weights
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huggingface-cli download 3DAIGC/LAM_audio2exp --local-dir ./
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tar -xzvf LAM_audio2exp_assets.tar && rm -f LAM_audio2exp_assets.tar
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tar -xzvf LAM_audio2exp_streaming.tar && rm -f LAM_audio2exp_streaming.tar
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# Or OSS Download (In case of HuggingFace download failing)
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# Download Assets
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wget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/data/LAM/LAM_audio2exp_assets.tar
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tar -xzvf LAM_audio2exp_assets.tar && rm -f LAM_audio2exp_assets.tar
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# Download Model Weights
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wget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/data/LAM/LAM_audio2exp_streaming.tar
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tar -xzvf LAM_audio2exp_streaming.tar && rm -f LAM_audio2exp_streaming.tar
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```
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### Quick Start Guide
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#### Using <a href="https://github.com/gradio-app/gradio">Gradio</a> Interface:
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We provide a simple Gradio demo with **WebGLGL Render**, and you can get rendering results by uploading audio in seconds.
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<img src="./assets/images/snapshot.png" alt="teaser" width="1000"/>
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```
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python app_lam_audio2exp.py
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```
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### Inference
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```bash
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# example: python inference.py --config-file configs/lam_audio2exp_config_streaming.py --options save_path=exp/audio2exp weight=pretrained_models/lam_audio2exp_streaming.tar audio_input=./assets/sample_audio/BarackObama_english.wav
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python inference.py --config-file ${CONFIG_PATH} --options save_path=${SAVE_PATH} weight=${CHECKPOINT_PATH} audio_input=${AUDIO_INPUT}
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```
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### Acknowledgement
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This work is built on many amazing research works and open-source projects:
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- [FLAME](https://flame.is.tue.mpg.de)
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- [FaceFormer](https://github.com/EvelynFan/FaceFormer)
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- [Meshtalk](https://github.com/facebookresearch/meshtalk)
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- [Unitalker](https://github.com/X-niper/UniTalker)
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- [Pointcept](https://github.com/Pointcept/Pointcept)
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Thanks for their excellent works and great contribution.
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### Related Works
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Welcome to follow our other interesting works:
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- [LAM](https://github.com/aigc3d/LAM)
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- [LHM](https://github.com/aigc3d/LHM)
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### Citation
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```
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@inproceedings{he2025LAM,
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title={LAM: Large Avatar Model for One-shot Animatable Gaussian Head},
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author={
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Yisheng He and Xiaodong Gu and Xiaodan Ye and Chao Xu and Zhengyi Zhao and Yuan Dong and Weihao Yuan and Zilong Dong and Liefeng Bo
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},
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booktitle={arXiv preprint arXiv:2502.17796},
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year={2025}
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}
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```
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