High-Quality Animatable Dynamic Garment Reconstruction from Monocular Videos

• URL: http://arxiv.org/abs/2311.01214v1
• Date: Thu, 2 Nov 2023 13:16:27 GMT
• Title: High-Quality Animatable Dynamic Garment Reconstruction from Monocular Videos
• Authors: Xiongzheng Li, Jinsong Zhang, Yu-Kun Lai, Jingyu Yang, Kun Li
• Abstract summary: 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. We show that our method can reconstruct high-quality dynamic garments with coherent surface details, which can be easily animated under unseen poses.
• Score: 51.8323369577494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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