Gong, Deshan;
Mao, Ningtao;
Wang, He;
(2024)
Bayesian Differentiable Physics for Cloth Digitalization.
In:
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
(pp. pp. 11841-11851).
IEEE: Seattle, WA, USA.
Preview |
Text
Bayesian Differentiable Physics for Cloth Digitalization.pdf - Accepted Version Download (2MB) | Preview |
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. Code and data are available11. https://github.com/realcrane/Bayesian-Differentiable-Physics-for-Cloth-Digitalization
| Type: | Proceedings paper |
|---|---|
| Title: | Bayesian Differentiable Physics for Cloth Digitalization |
| Event: | 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
| Location: | WA, Seattle |
| Dates: | 16 Jun 2024 - 22 Jun 2024 |
| ISBN-13: | 979-8-3503-5300-6 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/CVPR52733.2024.01125 |
| Publisher version: | https://doi.org/10.1109/cvpr52733.2024.01125 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Deep learning; Computer vision; Accuracy; Protocols; Codes; Current measurement; Data models |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10203951 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
