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license: apache-2.0
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language:
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- en
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pipeline_tag: image-to-3d
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<div align="center">
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<h1>LHM: Large Animatable Human Reconstruction Model for Single Image to 3D in Seconds</h1>
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<div align="center" style="display: flex; justify-content: center; flex-wrap: wrap;">
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<!-- <a href='LICENSE'><img src='https://img.shields.io/badge/license-MIT-yellow'></a> -->
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<a href='https://arxiv.org/pdf/2503.10625'><img src='https://img.shields.io/badge/๐-arXiv:2503-10625'></a>
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<a href='https://aigc3d.github.io/projects/LHM/'><img src='https://img.shields.io/badge/๐-Project_Website-blueviolet'></a>
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<a href='https://huggingface.co/spaces/3DAIGC/LHM'><img src='https://img.shields.io/badge/๐ค-HuggingFace_Space-blue'></a>
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<a href="https://www.apache.org/licenses/LICENSE-2.0"><img src="https://img.shields.io/badge/๐-Apache--2.0-929292"></a>
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</div>
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</div>
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## Overview
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This repository contains the models of the paper [LHM: Large Animatable Human Reconstruction Model
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for Single Image to 3D in Seconds](https://huggingface.co/papers/2503.10625).
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LHM is a feed-forward model for animatable 3D human reconstruction from a single image in seconds. Trained on a large-scale video
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dataset with an image reconstruction loss, our model exhibits strong generalization ability to diverse real-world scenarios
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## Quick Start
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Please refer to our [Github Repo](https://github.com/aigc3d/LHM/tree/main)
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### Download Model
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```python
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from huggingface_hub import snapshot_download
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# 1B Model
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model_dir = snapshot_download(model_id='3DAIGC/LHM-1B', cache_dir='./pretrained_models/huggingface')
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```
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## Citation
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```
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@inproceedings{qiu2025LHM,
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title={LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds},
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author={Lingteng Qiu and Xiaodong Gu and Peihao Li and Qi Zuo
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and Weichao Shen and Junfei Zhang and Kejie Qiu and Weihao Yuan
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and Guanying Chen and Zilong Dong and Liefeng Bo
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},
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booktitle={arXiv preprint arXiv:2503.10625},
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year={2025}
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}
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```
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