Perceiving and handling deformable objects is an integral part of everyday life for humans.
Automating tasks such as food handling, garment sorting, or assistive dressing requires …

Mesh-based simulations are central to modeling complex physical systems in many
disciplines across science and engineering. Mesh representations support powerful …

We propose a deep videorealistic 3D human character model displaying highly realistic
shape, motion, and dynamic appearance learned in a new weakly supervised way from …

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 propose a method that leverages graph neural networks, multi-level message passing,
and unsupervised training to enable real-time prediction of realistic clothing dynamics …

We present a general framework for the garment animation problem through unsupervised
deep learning inspired in physically based simulation. Existing trends in the literature …

Robotic manipulation of cloth remains challenging due to the complex dynamics of cloth,
lack of a low-dimensional state representation, and self-occlusions. In contrast to previous …

We present a new method for implicit time integration of physical systems. Our approach
builds a bridge between nodal Finite Element methods and Position Based Dynamics …

We present the first image-based generative model of people in clothing for the full body. We
sidestep the commonly used complex graphics rendering pipeline and the need for high …

This paper presents a learning‐based clothing animation method for highly efficient virtual
try‐on simulation. Given a garment, we preprocess a rich database of physically‐based …