Gong, D., Mao, N. orcid.org/0000-0003-1203-9773 and Wang, H. (2024) Bayesian Differentiable Physics for Cloth Digitalization. In: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) Annual Conference 2024, 17-21 Jun 2024, Seattle, WA. IEEE ISBN 979-8-3503-5301-3
Abstract
We propose a new method for cloth digitalization. Deviating from existing methods which learn from data captured under relatively casual settings, we propose to learn from data captured in strictly tested measuring protocols, and find plausible physical parameters of the cloths. However, such data is currently absent, so we first propose a new dataset with accurate cloth measurements. Further, the data size is considerably smaller than the ones in current deep learning, due to the nature of the data capture process. To learn from small data, we propose a new Bayesian differentiable cloth model to estimate the complex material heterogeneity of real cloths. It can provide highly accurate digitalization from very limited data samples. Through exhaustive evaluation and comparison, we show our method is accurate in cloth digitalization, efficient in learning from limited data samples, and general in capturing material variations.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Arts, Humanities and Cultures (Leeds) > School of Design (Leeds) |
Funding Information: | Funder Grant number AHRC (Arts & Humanities Research Council) AH/S002812/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 08 Aug 2024 10:18 |
Last Modified: | 30 Sep 2024 12:57 |
Published Version: | https://ieeexplore.ieee.org/document/10656614 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/CVPR52733.2024.01125 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:215818 |