Cloth Simulation

In this work, we propose to adopt a simulation-in-the-loop strategy for fabric parameter estimation and we train a regression-based neural network for inferring bending stiffness parameters from Cusick drap tests.

In this paper, we study physics-based cloth simulation in a very high resolution setting, presumably at submillimeter levels with millions of vertices, to meet perceptual precision of our human eyes.

In this article, we focus our research on the safety, efficiency, and realism of the repulsion-based collision handling approach, which has demonstrated its potential in existing GPU-based simulators.

Real-world cloth exhibits complex behaviors when it contacts deformable bodies. In this paper, we study how to improve the simulation of cloth-body interactions from three perspectives: collision, friction, and air pressure.

We propose a piecewise linear elastic model that approximates nonlinear, anisotropic stretching and bending behaviors of various materials. We develop new measurement techniques for studying the elastic deformations in real cloth samples.

Based on the observation that the wrinkles in close-fitting clothing behave in a predominantly kinematic fashion, we have developed an example-based wrinkle synthesis technique.