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High-quality animatable dynamic garment reconstruction from monocular videos

Li, Xiongzheng, Zhang, Jinsong, Lai, Yu-Kun ORCID: https://orcid.org/0000-0002-2094-5680, Yang, Jingyu and Li, Kun 2023. High-quality animatable dynamic garment reconstruction from monocular videos. IEEE Transactions on Circuits and Systems for Video Technology 10.1109/TCSVT.2023.3329972

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Abstract

Much progress has been made in reconstructing garments from an image or a video. However, none of existing works meet the expectations of digitizing high-quality animatable dynamic garments that can be adjusted to various unseen poses. In this paper, we propose the first method to recover high-quality animatable dynamic garments from monocular videos without depending on scanned data. To generate reasonable deformations for various unseen poses, we propose a learnable garment deformation network that formulates the garment reconstruction task as a pose-driven deformation problem. To alleviate the ambiguity estimating 3D garments from monocular videos, we design a multi-hypothesis deformation module that learns spatial representations of multiple plausible deformations. Experimental results on several public datasets demonstrate that our method can reconstruct high-quality dynamic garments with coherent surface details, which can be easily animated under unseen poses. The code will be provided for research purposes.

Item Type: Article
Date Type: Published Online
Status: In Press
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 1051-8215
Date of First Compliant Deposit: 10 November 2023
Date of Acceptance: 26 October 2023
Last Modified: 14 Nov 2023 11:53
URI: https://orca.cardiff.ac.uk/id/eprint/163784

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