DiffCloth

Customized by V-Sekai

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

📃 Paper | 🌍 Project

Tested Operating Systems

Ubuntu 22.04 | Mac OS 12

1. Download the repo:

2. Build CPP code with Cmake:

From the top directory:

mkdir build
cd build
cmake ..
make

3. Optimize/Visualize Section 6 Experiments:

• 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:

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

• Visualize optimization iters:

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

The progress of the optimization is saved into the output/ directory of the root folder.

Citation

Please consider citing our paper if your find our research or this codebase 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}
}