We present a self-supervised method to learn dynamic 3D deformations of garments worn
by parametric human bodies. State-of-the-art data-driven approaches to model 3D garment …

We present a novel, fast differentiable simulator for soft-body learning and control
applications. Existing differentiable soft-body simulators can be classified into two categories …

We extend the incremental potential contact (IPC) model for contacting elastodynamics to
resolve systems composed of codimensional DOFs in arbitrary combination. This enables a …

Cloth simulation has wide applications in computer animation, garment design, and robot-
assisted dressing. This work presents a differentiable cloth simulator whose additional …

A geometric mapping establishes a correspondence between two domains. Since no real
object has zero or negative volume, such a mapping is required to be inversion-free …

We present a methodology to automatically obtain Pose Space Deformation (PSD) basis for
rigged garments through deep learning. Classical approaches rely on Physically Based …

Recent approaches to drape garments quickly over arbitrary human bodies leverage self-
supervision to eliminate the need for large training sets. However, they are designed to train …

Many approaches to draping individual garments on human body models are realistic, fast,
and yield outputs that are differentiable with respect to the body shape on which they are …

We present two new approaches for animating dynamic fracture involving large elastoplastic
deformation. In contrast to traditional mesh-based techniques, where sharp discontinuity is …

We present a method for differentiable simulation of soft articulated bodies. Our work
enables the integration of differentiable physical dynamics into gradient-based pipelines …