CVPR2024
Bayesian Differentiable Physics for Cloth Digitalization
Deshan Gong, Ningtao Mao, He Wang
Abstract
Figure 1. We introduce a Bayesian Differentiable Physics (BPD) model for digitalizing real cloths by inferring their physical properties from the standard Cusick drape data (a-1, b-1, c-1 left). The digitalized cloths exhibit various drapabilities, faithfully reflecting their diverse mechanical characteristics and materials (a-1, b-1, c-1 middle and right). Further, our model enables the generalization of the learned mechanical characteristics and materials to garments (a-2, b-2, c-2).