python3 -m venv .venv
source .venv/bin/activate
pip install -U pip setuptools
pip install numpy open3d einops hesiod fvcore tensorboard trimesh cython networkx

pip install https://download.pytorch.org/whl/cu113/torch-1.12.0%2Bcu113-cp38-cp38-linux_x86_64.whl
pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py38_cu113_pyt1120/download.html

cd meshudf
source setup.sh

bash download_smpl.sh

cd docker/ && docker build -t drapenet . && cd ..

docker run -it --rm --gpus all --shm-size=8gb --name drapenet drapenet

bash download_smpl.sh

cd encdec
python3 preprocess_udf.py </path/to/meshes> </out/path>
python3 train_encdec.py  # add </path/to/run/cfg/file> to restore training
python3 export_codes.py </path/to/run/cfg/file>
python3 export_meshes.py </path/to/run/cfg/file>

cd editing
python3 create_dset.py
python3 compute_weights.py
python3 edit.py

cd draping
python3 drape.py

@inproceedings{de2023drapenet,
  author = {De Luigi, Luca and Li, Ren and Guillard, Benoit and Salzmann, Mathieu and Fua, Pascal},
  title = {{DrapeNet: Garment Generation and Self-Supervised Draping}},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year = {2023}
}