LHM_Track: Build Your LHM Training Dataset
Lingteng Qiu*, Xiaodong Gu*, Peihao Li*, Qi Zuo*, Weichao Shen, Junfei Zhang, Kejie Qiu, Weihao Yuan
Guanying Chen+, Zilong Dong+, Liefeng Bo
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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[April 8, 2025] Release the pipeline for building LHM training dataset.
Clone the repository.
git clone https://github.com/aigc3d/LHM_Track.git
cd LHM_TrackThe 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.shThe installation has been tested with python3.10, CUDA 12.1 or CUDA 11.8.
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 python track_video.py --video_path ${VIDEO_PATH} --output_path ${OUTPUT_PATH}@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}
}
This work is built on many amazing research works and open-source projects:
Thanks for their excellent works and great contribution to 3D generation and 3D digital human area.