Deep Deformation Detail Synthesis for Thin Shell Models

doi:10.1111/cgf.14903

	Deep Deformation Detail Synthesis for Thin Shell Models
	Chen, Lan 1,2; Gao, Lin 1,3; Yang, Jie 1,3; Xu, Shibiao 4; Ye, Juntao 2; Zhang, Xiaopeng 2; Lai, Yu-Kun 5
	2023-08-10
发表期刊	COMPUTER GRAPHICS FORUM
ISSN	0167-7055
页码	13
摘要	In physics-based cloth animation, rich folds and detailed wrinkles are achieved at the cost of expensive computational resources and huge labor tuning. Data-driven techniques make efforts to reduce the computation significantly by utilizing a preprocessed database. One type of methods relies on human poses to synthesize fitted garments, but these methods cannot be applied to general cloth animations. Another type of methods adds details to the coarse meshes obtained through simulation, which does not have such restrictions. However, existing works usually utilize coordinate-based representations which cannot cope with large-scale deformation, and requires dense vertex correspondences between coarse and fine meshes. Moreover, as such methods only add details, they require coarse meshes to be sufficiently close to fine meshes, which can be either impossible, or require unrealistic constraints to be applied when generating fine meshes. To address these challenges, we develop a temporally and spatially as-consistent-as-possible deformation representation (named TS-ACAP) and design a DeformTransformer network to learn the mapping from low-resolution meshes to ones with fine details. This TS-ACAP representation is designed to ensure both spatial and temporal consistency for sequential large-scale deformations from cloth animations. With this TS-ACAP representation, our DeformTransformer network first utilizes two mesh-based encoders to extract the coarse and fine features using shared convolutional kernels, respectively. To transduct the coarse features to the fine ones, we leverage the spatial and temporal Transformer network that consists of vertex-level and frame-level attention mechanisms to ensure detail enhancement and temporal coherence of the prediction. Experimental results show that our method is able to produce reliable and realistic animations in various datasets at high frame rates with superior detail synthesis abilities compared to existing methods.
关键词	Computing methodologies Physical simulation Artificial intelligence
DOI	10.1111/cgf.14903
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[U21A20515] ; National Natural Science Foundation of China[62171321] ; National Natural Science Foundation of China[62162044] ; National Natural Science Foundation of China[62102414] ; National Natural Science Foundation of China[62061136007] ; Beijing Municipal Natural Science Foundation for Distinguished Young Scholars[JQ21013] ; Royal Society Newton Advanced Fellowship[NAF\R2\192151] ; Innovation Funding of ICT, CAS[E361090]
WOS研究方向	Computer Science
WOS类目	Computer Science, Software Engineering
WOS记录号	WOS:001046199300001
出版者	WILEY
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21358
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Gao, Lin; Xu, Shibiao
作者单位	1.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 4.Beijing Univ Posts & Telecommun, Beijing, Peoples R China 5.Cardiff Univ, Cardiff, Wales
推荐引用方式 GB/T 7714	Chen, Lan,Gao, Lin,Yang, Jie,et al. Deep Deformation Detail Synthesis for Thin Shell Models[J]. COMPUTER GRAPHICS FORUM,2023:13.
APA	Chen, Lan.,Gao, Lin.,Yang, Jie.,Xu, Shibiao.,Ye, Juntao.,...&Lai, Yu-Kun.(2023).Deep Deformation Detail Synthesis for Thin Shell Models.COMPUTER GRAPHICS FORUM,13.
MLA	Chen, Lan,et al."Deep Deformation Detail Synthesis for Thin Shell Models".COMPUTER GRAPHICS FORUM (2023):13.