Learning 3D Garment Animation from Trajectories of A Piece of Cloth

[Paper]

This is the official repository of "Learning 3D Garment Animation from Trajectories of A Piece of Cloth, NeurIPS 2024".

Authors: Yidi Shao, Chen Change Loy, and Bo Dai.

Acknowedgement: This study is supported under the RIE2020 Industry Alignment Fund Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contributions from the industry partner(s). It is also supported by Singapore MOE AcRF Tier 2 (MOE-T2EP20221-0011) and partially funded by the Shanghai Artificial Intelligence Laboratory.

Feel free to ask questions. I am currently working on some other stuff but will try my best to reply. Please don't hesitate to star!

News

• 13 Dec, 2024: EUNet core codes released
• 24 Nov, 2024: Codes released

Table of Content

1. Video Demos
2. Dataset
3. Code
4. Citations

Video Demos

Dataset (Coming Soon)

While the dataset is not one of the main contribution, we will release part of the data including a piece of cloth.

Code

We train our model with 1 V100.

Installation

# CUDA11.7 TORCH1.13
conda create -n EUNet python=3.9 pytorch==1.13.0 pytorch-cuda=11.7 torchvision==0.14.0 torchaudio==0.13.0 -c pytorch -c nvidia -y
conda activate EUNet

<!-- mim install mmcv-full==1.7.1 -->
pip install mmcv-full==1.7.1 -f https://download.openmmlab.com/mmcv/dist/cu117/torch1.13.0/index.html

pip3 install h5py pyrender trimesh numpy==1.24.3 tqdm plotly scipy chumpy einops smplx yapf==0.40.1 tensorboard
pip install  dgl==1.1.0 -f https://data.dgl.ai/wheels/cu117/repo.html
pip install  dglgo -f https://data.dgl.ai/wheels-test/repo.html

pip3 install -v -e .

ln -s path/to/data data
ln -s path/to/work_dirs work_dirs

conda install -c fvcore -c iopath -c conda-forge fvcore iopath -y
conda install -c bottler nvidiacub -y
conda install pytorch3d -c pytorch3d -y

Training (EUNet)

python tools/train.py configs/potential_energy/base.py --work_dir PATH/TO/DIR

Inference (EUNet)

python tools/test.py configs/potential_energy/base.py PATH/TO/CHECKPOINT

Citations

@inproceedings{shao2024eunet,
  author = {Shao, Yidi and Loy, Chen Change and Dai, Bo},
  title = {Learning 3D Garment Animation from Trajectories of A Piece of Cloth},
  booktitle = {NeurIPS},
  year = {2024}
}