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A Pixel-Based Framework for Data-Driven Clothing

Ning Jin, Yilin Zhu, Zhenglin Geng, Ronald Fedkiw

With the aim of creating virtual cloth deformations more similar to real world clothing, we propose a new computational framework that recasts three dimensional cloth deformation as an RGB image in a two dimensional pat-tern space. Then a three dimensional animation of cloth is equivalent to a sequence of two dimensional RGB images,which in turn are driven/choreographed via animation parameters such as joint angles. This allows us to leverage popular CNNs to learn cloth deformations in image space.The two dimensional cloth pixels are extended into the real world via standard body skinning techniques, after which the RGB values are interpreted as texture offsets and dis-placement maps. 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, GANs for merging partitioned image regions back together, etc., can readily be incorporated into our framework.

A Pixel-Based Framework for Data-Driven Clothing

Posted by christopherbatty on October 6th, 2020 Posted in Uncategorized
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