A Pixel‐Based Framework for Data‐Driven Clothing. (24th November 2020)

• Record Type: Journal Article
• Authors: Jin, N.; Zhu, Y.; Geng, Z.; Fedkiw, R.
• Is Part Of: Computer graphics forum. Volume 39:Number 8(2020)
• Page Start: 135
• Page End: 144
• Publication Date: 2020-11-24
• Subjects: CCS Concepts -- Computing methodologies → Animation -- Neural networks -- Computer vision representations; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.14108
• Languages: English
• ISSNs: 0167-7055

Abstract

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 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.