Nonlinear Subspace Cloth Simulation

Author Qingyang Tan ( Abstract In this project, we want to build a cloth simulator in a nonlinear subspace to reduce the search space. We leverage the graph-based convolutional neural network (GCNN) with a physical-inspired loss term to build an embedding network for cloth models. We then use mass-spring systems or finite-element method (FEM) to model the cloth energy and build a simulator based on the nonlinear embedded space. We show that using the proposed method can better capture cloth deformation and generalize on new control information well.

Realtime Simulation of Thin-Shell Deformable Materials using CNN-Based Mesh Embedding

We address the problem of accelerating thin-shell deformable object simulations by dimension reduction. We present a new algorithm to embed a high-dimensional configuration space of deformable objects in a low-dimensional feature space, where the configurations of objects and feature points have approximate one-to-one mapping.