- Clone UP2You.
git clone https://github.com/zcai0612/UP2You.git cd UP2You- Create the environment.
conda create -n up2you python=3.10 conda activate up2you # torch pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu118 # kaolin pip install kaolin==0.17.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.4.1_cu118.html pip install -r requirements.txt # https://github.com/cnr-isti-vclab/meshlab/issues/1461 conda install -y libffi==3.3 # install pytorch3d, download from conda wget https://anaconda.org/pytorch3d/pytorch3d/0.7.8/download/linux-64/pytorch3d-0.7.8-py310_cu118_pyt241.tar.bz2 conda install pytorch3d-0.7.8-py310_cu118_pyt241.tar.bz2Download pretrained_models and human_models from huggingface Co2y/UP2You, and put them into the project directory. You can refer to following commands:
export HF_ENDPOINT="https://hf-mirror.com" # Optional hf download Co2y/UP2You --local-dir ./src # download models mv ./src/human_models ./ mv ./src/pretrained_models ./ rm -rf ./srcTo run the inference pipeline, you can use the following command:
python inference_low_gpu.py \ --base_model_path stabilityai/stable-diffusion-2-1-base \ --segment_model_name ZhengPeng7/BiRefNet \ --data_dir examples \ --output_dir outputs \or you can just use run.sh:
bash run.sh Here we provide an example, where examples is the folder of unconstrained photos and outputs is the output directory of generated results.
Our code is based on the following awesome repositories and datasets:
- MV-Adapter, PuzzleAvatar, VGGT, PSHuman, SOAP
- THuman2.1, CustomHumans, 2k2k, Human4DiT, 4D-Dress, PuzzleIOI
We thank the authors for releasing their code and data !
We thank Siyuan Yu for the help in Houdini Simulation, Shunsuke Saito, Dianbing Xi, Yifei Zeng for the fruitful discussions, and the members of Endless AI Lab for their help on data capture and discussions.
If you find our work useful, please cite:
@article{cai2025up2you, title={UP2You: Fast Reconstruction of Yourself from Unconstrained Photo Collections}, author={Cai, Zeyu and Li, Ziyang and Li, Xiaoben and Li, Boqian and Wang, Zeyu and Zhang, Zhenyu and Xiu, Yuliang}, journal={arXiv preprint arXiv:2509.24817}, year={2025} }