The realm of textiles spans clothing, households, healthcare, sports, and industrial
applications. The deformable nature of these objects poses unique challenges that prior …

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 …

We propose a differentiable cloth simulator that can be embedded as a layer in deep neural
networks. This approach provides an effective, robust framework for modeling cloth …

We propose a new generative model for 3D garment deformations that enables us to learn,
for first time, a data-driven method for virtual try-on that effectively addresses garment-body …

Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the
robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level …

Assistive robots have the potential to support people with disabilities in a variety of activities
of daily living, such as dressing. People who have completely lost their upper limb …

We propose a scalable neural network framework to reconstruct the 3D mesh of a human
body from multi-view images, in the subspace of the SMPL model. Use of multi-view images …

This paper introduces a methodology for inverse-modeling of yarn-level mechanics of cloth,
based on the mechanical response of fabrics in the real world. We compiled a database …

Real-world fabrics often possess complicated nonlinear, anisotropic bending stiffness
properties. Measuring the physical parameters of such properties for physics-based …