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Research Article | Open Access

CTSN: Predicting cloth deformation for skeleton-based characters with a two-stream skinning network

College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
Aurora Studios, Tencent, Shenzhen 518057, China
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Graphical Abstract

Abstract

We present a novel learning method using a two-stream network to predict cloth deformation for skeleton-based characters. The characters processed in our approach are not limited to humans, and can be other targets with skeleton-based representations such as fish or pets. We use a novel network architecturewhich consists of skeleton-based and mesh-based residual networks to learn the coarse features and wrinkle features forming the overall residual from the template cloth mesh. Our network may be used to predict the deformation for loose or tight-fitting clothing. The memory footprint of our network is low, thereby resulting in reduced computational requirements. In practice, a prediction for a single cloth mesh for a skeleton-based character takes about 7 ms on an nVidia GeForce RTX 3090 GPU. Compared to prior methods, our network can generate finer deformation results with details and wrinkles.

Keywords

cloth deformationlearning networkskinning

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Computational Visual Media
Volume 10 Issue 3,
June 2024
Pages 471-485
DOI: 10.1007/s41095-023-0344-6
Cite this article:
Li Y, Tang M, Yang Y, et al. CTSN: Predicting cloth deformation for skeleton-based characters with a two-stream skinning network. Computational Visual Media, 2024, 10(3): 471-485. https://doi.org/10.1007/s41095-023-0344-6

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Received: 14 January 2023
Accepted: 20 March 2023
Published: 19 April 2024
© The Author(s) 2024.

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