Code repository for our paper DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact

Make sure to use the --recursive option to install the dependencies

git clone --recursive https://github.com/omegaiota/DiffCloth.git

From the top directory:

mkdir build
cd build
cmake ..
make

• Run optimization:

  ./DiffCloth -demo {demooptions} -mode optimize -seed {randseed}
  

  where {demooptions} is the name of the demos from the following options and {randseed} is an integer for random initialization of the initial guesses of the tasks.

  The corresponding option for each of the experiments is:

  • 6.1 T-shirt: tshirt
  • 6.1 Sphere: sphere
  • 6.2 Hat: hat
  • 6.2 Sock: sock
  • 6.3 Dress: dress

• Visualize optimization iters:

  ./DiffCloth -demo {demooptions} -mode visualize -exp {expName}
  

  where {expName} is the iteration folder for visualization. The code repo comes with an example optimization run of T-shirt in output/tshirt-exampleopt/, and you can visualize the first iteration with

  ./DiffCloth -demo tshirt -mode visualize -exp tshirt-exampleopt/iter0/
  

The progress of the optimization is saved into the output/ directory of the root folder. Intermediate progress are visualized using the custom written OpenGL viewer.

Build Python Binding:

• Install anaconda for virtual environment.
• In project root folder, run python setup.py install to install the python binding package. Rerun this command if you modify the CPP code.
• Create conda virtual environment: conda env create python=3.8 --file environment.yml, and activate it through conda activate diffcloth

Train/Test Hat Controller example:

• Navigate to src/python_code

• Test pretrained network: run python hatController.py --eval --render --load_expname 20210809-trainedBest
• Train network: run python hatController.py --render
• Resume train: run python hatController.py --train_resume --load_expname [expName] --load_epoch [epochNum]

Simulations are saved to output/ directory of the root folder.

Feel free to contact me or creating a Github issue if you have questions regarding setting up the repository, running examples or setting up new examples.

Please consider citing our paper if your find our research helpful:

@article{Li2022diffcloth,
author = {Li, Yifei and Du, Tao and Wu, Kui and Xu, Jie and Matusik, Wojciech},
title = {DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact},
year = {2022},
issue_date = {February 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {42},
number = {1},
issn = {0730-0301},
url = {https://doi.org/10.1145/3527660},
doi = {10.1145/3527660},
abstract = {Cloth simulation has wide applications in computer animation, garment design, and robot-assisted dressing. This work presents a differentiable cloth simulator whose additional gradient information facilitates cloth-related applications. Our differentiable simulator extends a state-of-the-art cloth simulator based on Projective Dynamics (PD) and with dry frictional contact [Ly et al. 2020]. We draw inspiration from previous work [Du et al. 2021] to propose a fast and novel method for deriving gradients in PD-based cloth simulation with dry frictional contact. Furthermore, we conduct a comprehensive analysis and evaluation of the usefulness of gradients in contact-rich cloth simulation. Finally, we demonstrate the efficacy of our simulator in a number of downstream applications, including system identification, trajectory optimization for assisted dressing, closed-loop control, inverse design, and real-to-sim transfer. We observe a substantial speedup obtained from using our gradient information in solving most of these applications.},
journal = {ACM Trans. Graph.},
month = {oct},
articleno = {2},
numpages = {20},
keywords = {differentiable simulation, cloth simulation, Projective Dynamics}
}