Inverse simulation: Reconstructing dynamic geometry of clothed humans via optimal control

Jingfan Guo, Jie Li, Rahul Narain, Hyun Soo Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
PublisherIEEE Computer Society
Pages14693-14702
Number of pages10
ISBN (Electronic)9781665445092
DOIs
StatePublished - 2021
Event2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021 - Virtual, Online, United States
Duration: Jun 19 2021Jun 25 2021

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Conference

Conference2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
Country/TerritoryUnited States
CityVirtual, Online
Period6/19/216/25/21

Bibliographical note

Funding Information:
Acknowledgements This work is supported by NSF CAREER IIS-1846031 and NSF CNS-1919965.

Publisher Copyright:
© 2021 IEEE

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