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A Pixel‐Based Framework for Data‐Driven Clothing
Author(s) -
Jin N.,
Zhu Y.,
Geng Z.,
Fedkiw R.
Publication year - 2020
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.14108
Subject(s) - computer science , artificial intelligence , skinning , computer vision , morphing , convolutional neural network , leverage (statistics) , rgb color model , pixel , animation , computer graphics (images) , ecology , biology
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 two‐dimensional 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.