A Pixel‐Based Framework for Data‐Driven Clothing

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‐...

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Veröffentlicht in:Computer graphics forum Jg. 39; H. 8; S. 135 - 144
Hauptverfasser: Jin, N., Zhu, Y., Geng, Z., Fedkiw, R.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Blackwell Publishing Ltd 01.12.2020
Schlagworte:
Animation
Artificial neural networks
CCS Concepts
Cloth
Color imagery
Computer vision representations
Computing methodologies → Animation
Data structures
Deformation
Euclidean geometry
Neural networks
Offsets
Triangles
ISSN:0167-7055, 1467-8659
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:Work done while at Stanford University
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.14108