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

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LAM is a cutting-edge project that enables the creation of ultra-realistic, animatable 3D avatars from just a single image in seconds. Leveraging advanced Gaussian Head technology, it offers super-fast cross-platform rendering and a low-latency SDK for real-time interactive chatting. This innovative model is set to be presented at SIGGRAPH 2025.

GitHub: https://github.com/aigc3d/LAM
OSRepos URL: https://osrepos.com/repo/aigc3d-lam

## Summary

LAM is a cutting-edge project that enables the creation of ultra-realistic, animatable 3D avatars from just a single image in seconds. Leveraging advanced Gaussian Head technology, it offers super-fast cross-platform rendering and a low-latency SDK for real-time interactive chatting. This innovative model is set to be presented at SIGGRAPH 2025.

## Topics

- Python
- 3D Avatar
- Gaussian Splatting
- AI
- Computer Graphics
- Real-time
- Animatable Head
- SIGGRAPH

## Repository Information

Last analyzed by OSRepos: Sun Dec 07 2025 08:00:59 GMT+0000 (Western European Standard Time)
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## Content

## Introduction

LAM, or Large Avatar Model, is an innovative project designed to revolutionize the creation of 3D digital humans. It allows users to generate ultra-realistic, animatable 3D avatars from a single image in mere seconds. This technology is particularly notable for its super-fast cross-platform animating and rendering capabilities, making it suitable for various devices. Furthermore, LAM provides a low-latency SDK, enabling the development of real-time interactive chatting avatars. The project is slated for presentation at SIGGRAPH 2025, highlighting its significance in the field of computer graphics and AI.

## Installation

Getting started with LAM is straightforward, with options for both Windows and Linux users.

### Online Demos

For a quick preview, you can explore the online demos:

*   **Avatar Generation from One Image:**
    *   [HuggingFace Space](https://huggingface.co/spaces/3DAIGC/LAM)
    *   [ModelScope Space](https://www.modelscope.cn/studios/Damo_XR_Lab/LAM_Large_Avatar_Model)
*   **Interactive Chatting:**
    *   [HuggingFace Space](https://huggingface.co/spaces/HumanAIGC-Engineering-Team/open-avatar-chat)
    *   [ModelScope Space](https://www.modelscope.cn/studios/HumanAIGC-Engineering/open-avatar-chat)

### Environment Setup

**Windows One-Click Installer:**

A convenient one-click installation package for Windows (Cuda 12.8) is available. You can find a [video guide](https://www.bilibili.com/video/BV13QGizqEey) and the [download link](https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/data/LAM/Installation/LAM-windows-one-click-install.zip).

**Linux:**

bash
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 (Manual):**

For manual installation on Windows, please refer to the [Windows Install Guide](https://github.com/aigc3d/LAM/blob/master/scripts/install/WINDOWS_INSTALL.md).

### Model Weights

Model weights can be downloaded from HuggingFace or ModelScope.

**HuggingFace Download:**

bash
# 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:**

bash
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/"


## Examples

After setting up the environment, you can run LAM locally.

### Gradio Run

To run the Gradio demo:

bash
python app_lam.py


If you wish to export ZIP files for real-time conversations on OpenAvatarChat, refer to the [Avatar Export Guide](https://github.com/aigc3d/LAM/blob/master/tools/AVATAR_EXPORT_GUIDE.md) and run with the Blender path:

bash
python app_lam.py --blender_path /path/blender


### Inference

For inference, use the provided script:

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


## Why Use LAM?

LAM offers compelling advantages for anyone interested in 3D avatar creation and interactive AI applications:

*   **Ultra-realistic 3D Avatar Creation from One Image in Seconds:** Quickly generate high-fidelity avatars with minimal input.
*   **Super-fast Cross-platform Animating and Rendering on Any Devices:** Experience smooth performance across various hardware, from powerful GPUs to mobile phones.
*   **Low-latency SDK for Realtime Interactive Chatting Avatar:** Build responsive and engaging conversational AI experiences with integrated 3D digital humans.

## Links

*   **GitHub Repository:** [https://github.com/aigc3d/LAM](https://github.com/aigc3d/LAM)
*   **Project Website:** [https://aigc3d.github.io/projects/LAM/](https://aigc3d.github.io/projects/LAM/)
*   **arXiv Paper:** [https://arxiv.org/pdf/2502.17796](https://arxiv.org/pdf/2502.17796)
*   **HuggingFace Space (Avatar Generation):** [https://huggingface.co/spaces/3DAIGC/LAM](https://huggingface.co/spaces/3DAIGC/LAM)
*   **ModelScope Space (Avatar Generation):** [https://www.modelscope.cn/studios/Damo_XR_Lab/LAM_Large_Avatar_Model](https://www.modelscope.cn/studios/Damo_XR_Lab/LAM_Large_Avatar_Model)
*   **HuggingFace Space (Interactive Chatting):** [https://huggingface.co/spaces/HumanAIGC-Engineering-Team/open-avatar-chat](https://huggingface.co/spaces/HumanAIGC-Engineering-Team/open-avatar-chat)
*   **ModelScope Space (Interactive Chatting):** [https://www.modelscope.cn/studios/HumanAIGC-Engineering/open-avatar-chat](https://www.modelscope.cn/studios/HumanAIGC-Engineering/open-avatar-chat)
*   **OpenAvatarChat:** [https://github.com/HumanAIGC-Engineering/OpenAvatarChat](https://github.com/HumanAIGC-Engineering/OpenAvatarChat)