LAM-20K / README.md
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---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Library: [More Information Needed]
- Docs: [More Information Needed]
---
license: apache-2.0
- en
---
<div align="center">
<h1>LAM: Large Avatar Model for One-shot Animatable Gaussian Head</h1>
<div align="center" style="display: flex; justify-content: center; flex-wrap: wrap;">
<!-- <a href='LICENSE'><img src='https://img.shields.io/badge/license-MIT-yellow'></a> -->
<a href='https://arxiv.org/pdf/2502.17796'><img src='https://img.shields.io/badge/πŸ“œ-arXiv:2503-10625'></a>
<a href='https://aigc3d.github.io/projects/LAM/'><img src='https://img.shields.io/badge/🌐-Project_Website-blueviolet'></a>
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</div>
</div>
## Overview
This repository contains the models of the paper [LAM](https://arxiv.org/pdf/2502.17796).
LAM creates animatable Gaussian heads with one-shot images in a single forward pass in seconds. The reconstructed Gaussian avatar can
be reenacted and rendered on various platforms in real-time.
## Quick Start
Please refer to our [Github Repo](https://github.com/aigc3d/LAM)
### Download Model
```python
from huggingface_hub import snapshot_download
# LAM-20K Model
model_dir = snapshot_download(repo_id='3DAIGC/LAM-20K', cache_dir='./pretrained_models/huggingface/')
```
## Citation
```
@article{he2025lam,
title={LAM: Large Avatar Model for One-shot Animatable Gaussian Head},
author={He, Yisheng and Gu, Xiaodong and Ye, Xiaodan and Xu, Chao and Zhao, Zhengyi and Dong, Yuan and Yuan, Weihao and Dong, Zilong and Bo, Liefeng},
journal={arXiv preprint arXiv:2502.17796},
year={2025}
}
```