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LHM_Track is a video processing pipeline for building LHM training dataset. It generates multiple labels from human centric video, such as segmentation, 2D keypoints, SMPL-X parameters and FLAME parameters.
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📢 Latest Updates
[April 8, 2025] Release the pipeline for building LHM training dataset.
🚀 Getting Started
Environment Setup
Clone the repository.
git clone https://github.com/aigc3d/LHM_Track.git
cd LHM_Track
The environment is not same as the LHM repo. Please create a new conda environment or python venv.
# create new environment
conda create -n lhm_track python=3.10
conda activate lhm_track
# or# python -m venv lhm_track# source lhm_track/bin/activate# cuda 12.1
sh ./install_cu121.sh
# cuda 11.8
sh ./install_cu118.sh
The installation has been tested with python3.10, CUDA 12.1 or CUDA 11.8.
Download Prior Model Weights
Download basic model weights. If you've downloaded them in LHM repo, you can copy/link the pretrained_models folder into this repo.
# Download prior model weights
wget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/data/LHM/LHM_prior_model.tar
tar -xvf LHM_prior_model.tar
Download extra model weights.
# Download extra model weights
wget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/data/LHM/LHM_track_model.tar
tar -xvf LHM_track_model.tar
@inproceedings{qiu2025LHM,
title={LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds},
author={Lingteng Qiu and Xiaodong Gu and Peihao Li and Qi Zuo
and Weichao Shen and Junfei Zhang and Kejie Qiu and Weihao Yuan
and Guanying Chen and Zilong Dong and Liefeng Bo
},
booktitle={arXiv preprint arXiv:2503.10625},
year={2025}
}
Acknowledgement
This work is built on many amazing research works and open-source projects: