Inverse Simulation: Reconstructing Dynamic Geometry of Clothed Humans via Optimal Control

Jingfan Guo, Jie Li, Rahul Narain, Hyun Soo Park; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 14698-14707

This paper studies the problem of inverse cloth simulation---to estimate shape and time-varying poses of the underlying body that generates physically plausible cloth motion, which matches to the point cloud measurements on the clothed humans. A key innovation is to represent the dynamics of the cloth geometry using a dynamical system that is controlled by the body states (shape and pose). This allows us to express the cloth motion as a resultant of external (skin friction and gravity) and internal (elasticity) forces. Inspired by the theory of optimal control, we optimize the body states such that the simulated cloth motion is matched to the point cloud measurements, and the analytic gradient of the simulator is back-propagated to update the body states. We propose a cloth relaxation scheme to initialize the cloth state, which ensures the physical validity. Our method produces physically plausible and temporally smooth cloth and body movements that are faithful to the measurements, and shows superior performance compared to the existing methods. As a byproduct, the stress and strain that are applied to the body and clothes can be recovered.