LAM: Official Pytorch Implementation

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LAM: Large Avatar Model for One-shot Animatable Gaussian Head

SIGGRAPH 2025

Yisheng He*, Xiaodong Gu*, Xiaodan Ye, Chao Xu, Zhengyi Zhao, Yuan Dong†, Weihao Yuan†, Zilong Dong, Liefeng Bo

Tongyi Lab, Alibaba Group

"Build 3D Interactive Chatting Avatar with One Image in Seconds!"

Core Highlights 🔥🔥🔥

• Ultra-realistic 3D Avatar Creation from One Image in Seconds
• Super-fast Cross-platform Animating and Rendering on Any Devices
• Low-latency SDK for Realtime Interactive Chatting Avatar

Chat_Demo.mp4

📢 News

[May 20, 2025] We have released the WebGL-Render!

[May 10, 2025] The ModelScope Demo now supports directly exporting the generated Avatar to files required by OpenAvatarChat for interactive chatting!

[April 30, 2025] We have released a Avatar Export Feature that allows users to chat with any LAM-generated 3D digital humans on OpenAvatarChat. 🔥

[April 21, 2025] We have released the WebGL Interactive Chatting Avatar SDK on OpenAvatarChat (including LLM, ASR, TTS, Avatar), with which you can freely chat with the 3D Digital Human generated by LAM ! 🔥

[April 19, 2025] We have released the Audio2Expression model, which can animate the generated LAM Avatar with audio input ! 🔥

To do list

• Release LAM-small trained on VFHQ and Nersemble.
• Release Huggingface space.
• Release Modelscope space.
• Release LAM-large trained on a self-constructed large dataset.
• Release WebGL Render for cross-platform animation and rendering.
• Release audio driven model: Audio2Expression.
• Release Interactive Chatting Avatar SDK with OpenAvatarChat, including LLM, ASR, TTS, Avatar.

🚀 Get Started

Online Demo

Avatar Generation from One Image:

Interactive Chatting:

Environment Setup

We provide a one-click installation package on Windows (Cuda 12.8), supported by "十字鱼".     Video     Download Link

Linux:

git clone https://github.com/aigc3d/LAM.git
cd LAM
# Install with Cuda 12.1
sh ./scripts/install/install_cu121.sh
# Or Install with Cuda 11.8
sh ./scripts/install/install_cu118.sh

Windows:

For Windows, please refer to the Windows Install Guide.

Model Weights

Model	Training Data	HuggingFace	ModelScope	Reconstruction Time	A100 (A & R)	XiaoMi 14 Phone (A & R)
LAM-20K	VFHQ	TBD	TBD	1.4 s	562.9FPS	110+FPS
LAM-20K	VFHQ + NeRSemble	Link	Link	1.4 s	562.9FPS	110+FPS
LAM-20K	Our large dataset	TBD	TBD	1.4 s	562.9FPS	110+FPS

(A & R: Animating & Rendering )

HuggingFace Download

# Download Assets
huggingface-cli download 3DAIGC/LAM-assets --local-dir ./tmp
tar -xf ./tmp/LAM_assets.tar && rm ./tmp/LAM_assets.tar
tar -xf ./tmp/thirdparty_models.tar && rm -r ./tmp/
# Download Model Weights
huggingface-cli download 3DAIGC/LAM-20K --local-dir ./model_zoo/lam_models/releases/lam/lam-20k/step_045500/

ModelScope Download

pip3 install modelscope
# Download Assets
modelscope download --model "Damo_XR_Lab/LAM-assets" --local_dir "./tmp/"
tar -xf ./tmp/LAM_assets.tar && rm ./tmp/LAM_assets.tar
tar -xf ./tmp/thirdparty_models.tar && rm -r ./tmp/
# Download Model Weights
modelscope download "Damo_XR_Lab/LAM-20K" --local_dir "./model_zoo/lam_models/releases/lam/lam-20k/step_045500/"

Gradio Run

python app_lam.py

If you want to export ZIP files for real-time conversations on OpenAvatarChat, please refer to the Guide.

python app_lam.py --blender_path /path/blender

Inference

sh ./scripts/inference.sh ${CONFIG} ${MODEL_NAME} ${IMAGE_PATH_OR_FOLDER} ${MOTION_SEQ}

Acknowledgement

This work is built on many amazing research works and open-source projects:

• OpenLRM
• GAGAvatar
• GaussianAvatars
• VHAP

Thanks for their excellent works and great contribution.

More Works

Welcome to follow our other interesting works:

• LHM

Citation

@inproceedings{he2025LAM,
  title={LAM: Large Avatar Model for One-shot Animatable Gaussian Head},
  author={
    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
  },
  booktitle={SIGGRAPH},
  year={2025}
}