Subspace Clothing Simulation Using Adaptive Bases - Robotics Institute Carnegie Mellon University

Subspace Clothing Simulation Using Adaptive Bases

Fabian Hahn, Bernhard Thomaszewski, Stelian Coros, Robert W. Sumner, Forrester Cole, Mark Meyer, Tony DeRose, and Markus Gross
Journal Article, ACM Transactions on Graphics (TOG), Vol. 33, No. 4, July, 2014

Abstract

We present a new approach to clothing simulation using low-dimensional linear subspaces with temporally adaptive bases. Our method exploits full-space simulation training data in order to construct a pool of low-dimensional bases distributed across pose space. For this purpose, we interpret the simulation data as offsets from a kinematic deformation model that captures the global shape of clothing due to body pose. During subspace simulation, we select low-dimensional sets of basis vectors according to the current pose of the character and the state of its clothing. Thanks to this adaptive basis selection scheme, our method is able to reproduce diverse and detailed folding patterns with only a few basis vectors. Our experiments demonstrate the feasibility of subspace clothing simulation and indicate its potential in terms of quality and computational efficiency.

BibTeX

@article{Hahn-2014-17158,
author = {Fabian Hahn and Bernhard Thomaszewski and Stelian Coros and Robert W. Sumner and Forrester Cole and Mark Meyer and Tony DeRose and Markus Gross},
title = {Subspace Clothing Simulation Using Adaptive Bases},
journal = {ACM Transactions on Graphics (TOG)},
year = {2014},
month = {July},
volume = {33},
number = {4},
keywords = {physically-based simulation, subspace simulation},
}