no code implementations • 10 Nov 2023 • Jingfan Guo, Fabian Prada, Donglai Xiang, Javier Romero, Chenglei Wu, Hyun Soo Park, Takaaki Shiratori, Shunsuke Saito
Registering clothes from 4D scans with vertex-accurate correspondence is challenging, yet important for dynamic appearance modeling and physics parameter estimation from real-world data.
no code implementations • 30 Jun 2022 • Donglai Xiang, Timur Bagautdinov, Tuur Stuyck, Fabian Prada, Javier Romero, Weipeng Xu, Shunsuke Saito, Jingfan Guo, Breannan Smith, Takaaki Shiratori, Yaser Sheikh, Jessica Hodgins, Chenglei Wu
The key idea is to introduce a neural clothing appearance model that operates on top of explicit geometry: at training time we use high-fidelity tracking, whereas at animation time we rely on physically simulated geometry.
no code implementations • CVPR 2021 • Jingfan Guo, Jie Li, Rahul Narain, Hyun Soo Park
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.