A Pixel-Based Framework for Data-Driven Clothing
Loading...
Date
2020
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
We propose a novel approach to learning cloth deformation as a function of body pose, recasting the graph-like triangle mesh data structure into image-based data in order to leverage popular and well-developed convolutional neural networks (CNNs) in a two-dimensional Euclidean domain. Then, a three-dimensional animation of clothing is equivalent to a sequence of twodimensional RGB images driven/choreographed by time dependent joint angles. In order to reduce nonlinearity demands on the neural network, we utilize procedural skinning of the body surface to capture much of the rotation/deformation so that the RGB images only contain textures of displacement offsets from skin to clothing. Notably, we illustrate that our approach does not require accurate unclothed body shapes or robust skinning techniques. Additionally, we discuss how standard image based techniques such as image partitioning for higher resolution can readily be incorporated into our framework.
Description
@article{10.1111:cgf.14108,
journal = {Computer Graphics Forum},
title = {{A Pixel-Based Framework for Data-Driven Clothing}},
author = {Jin, Ning and Zhu, Yilin and Geng, Zhenglin and Fedkiw, Ron},
year = {2020},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14108}
}