Qingyang Tan (firstname.lastname@example.org)
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.