  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.
