Learning a shared deformation space for efficient design-preserving garment transfer

doi:10.1016/j.gmod.2021.101106

	Learning a shared deformation space for efficient design-preserving garment transfer
	Shi, Min 4; Wei, Yukun 4; Chen, Lan 2,3; Zhu, Dengming 1; Mao, Tianlu 1; Wang, Zhaoqi 1
刊名	GRAPHICAL MODELS
	2021-05-01
卷号	115 页码:12
关键词	Garment transfer Cloth deformation Shape analysis
ISSN号	1524-0703
DOI	10.1016/j.gmod.2021.101106
英文摘要	Garment transfer from a source mannequin to a shape-varying individual is a vital technique in computer graphics. Existing garment transfer methods are either time consuming or lack designed details especially for clothing with complex styles. In this paper, we propose a data-driven approach to efficiently transfer garments between two distinctive bodies while preserving the source design. Given two sets of simulated garments on a source body and a target body, we utilize the deformation gradients as the representation. Since garments in our dataset are with various topologies, we embed cloth deformation to the body. For garment transfer, the deformation is decomposed into two aspects, typically style and shape. An encoder-decoder network is proposed to learn a shared space which is invariant to garment style but related to the deformation of human bodies. For a new garment in a different style worn by the source human, our method can efficiently transfer it to the target body with the shared shape deformation, meanwhile preserving the designed details. We qualitatively and quantitatively evaluate our method on a diverse set of 3D garments that showcase rich wrinkling patterns. Experiments show that the transferred garments can preserve the source design even if the target body is quite different from the source one.
资助项目	National Natural Science Foundation of China[61972379]
WOS研究方向	Computer Science
语种	英语
出版者	ACADEMIC PRESS INC ELSEVIER SCIENCE
WOS记录号	WOS:000654031100002
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/17542]
专题	中国科学院计算技术研究所
通讯作者	Shi, Min
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 4.North China Elect Power Univ, Sch Control & Comp Engn, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Shi, Min,Wei, Yukun,Chen, Lan,et al. Learning a shared deformation space for efficient design-preserving garment transfer[J]. GRAPHICAL MODELS,2021,115:12.
APA	Shi, Min,Wei, Yukun,Chen, Lan,Zhu, Dengming,Mao, Tianlu,&Wang, Zhaoqi.(2021).Learning a shared deformation space for efficient design-preserving garment transfer.GRAPHICAL MODELS,115,12.
MLA	Shi, Min,et al."Learning a shared deformation space for efficient design-preserving garment transfer".GRAPHICAL MODELS 115(2021):12.