Differentiable Cloth Simulation for Inverse Problems


Abstract

We propose a differentiable cloth simulator that can be embedded as a layer in deep neural networks. This approach provides an effective, robust framework for modeling cloth dynamics, self-collisions, and contacts. Due to the high dimensionality of the dynamical system in modeling cloth, traditional gradient computation for collision response can become impractical. To address this problem, we propose to compute the gradient directly using QR decomposition of a much smaller matrix. Experimental results indicate that our method can speed up backpropagation by two orders of magnitude. We demonstrate the presented approach on a number of inverse problems, including parameter estimation and motion control for cloth.

Paper

Differentiable Cloth Simulation for Inverse Problems, NeurIPS 2019.
Junbang Liang, Ming C. Lin, Vladlen Koltun.

Video

Demo video can be found here

Code

The GitHub repository is located here.