Towards Fine-Grained Optimal 3D Face Dense Registration: An Iterative Dividing and Diffusing Method

doi:10.1007/s11263-023-01825-7

CASIA OpenIR > 智能制造技术与系统研究中心 > 多维数据分析（彭思龙）-技术团队

	Towards Fine-Grained Optimal 3D Face Dense Registration: An Iterative Dividing and Diffusing Method
	Fan, Zhenfeng1,3 ; Peng, Silong2,3 ; Xia, Shihong 1,3
发表期刊	INTERNATIONAL JOURNAL OF COMPUTER VISION
ISSN	0920-5691
	2023-06-03
页码	21
通讯作者	Xia, Shihong(xsh@ict.ac.cn)
摘要	Dense vertex-to-vertex correspondence (i.e. registration) between 3D faces is a fundamental and challenging issue for 3D &2D face analysis. While the sparse landmarks are definite with anatomically ground-truth correspondence, the dense vertex correspondences on most facial regions are unknown. In this view, the current methods commonly result in reasonable but diverse solutions, which deviate from the optimum to the dense registration problem. In this paper, we revisit dense registration by a dimension-degraded problem, i.e. proportional segmentation of a line, and employ an iterative dividing and diffusing method to reach an optimum solution that is robust to different initializations. We formulate a local registration problem for dividing and a linear least-square problem for diffusing, with constraints on fixed features on a 3D facial surface. We further propose a multi-resolution algorithm to accelerate the computational process. The proposed method is linked to a novel local scaling metric, where we illustrate the physical significance as smooth adaptions for local cells of 3D facial shapes. Extensive experiments on public datasets demonstrate the effectiveness of the proposed method in various aspects. Generally, the proposed method leads to not only significantly better representations of 3D facial data, but also coherent local deformations with elegant grid architecture for fine-grained registrations.
关键词	3D face Dense correspondence Non-rigid registration 3D morphable model
DOI	10.1007/s11263-023-01825-7
关键词[WOS]	POINT ; RECOGNITION ; RECONSTRUCTION ; DATABASE ; TRENDS
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2022YFF0902302] ; National Science Foundation of China[62106250] ; China Postdoctoral Science Foundation[2021M703272]
项目资助者	National Key Research and Development Program of China ; National Science Foundation of China ; China Postdoctoral Science Foundation
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000998738900001
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53424
专题	智能制造技术与系统研究中心_多维数据分析（彭思龙）-技术团队
通讯作者	Xia, Shihong
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Fan, Zhenfeng,Peng, Silong,Xia, Shihong. Towards Fine-Grained Optimal 3D Face Dense Registration: An Iterative Dividing and Diffusing Method[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2023:21.
APA	Fan, Zhenfeng,Peng, Silong,&Xia, Shihong.(2023).Towards Fine-Grained Optimal 3D Face Dense Registration: An Iterative Dividing and Diffusing Method.INTERNATIONAL JOURNAL OF COMPUTER VISION,21.
MLA	Fan, Zhenfeng,et al."Towards Fine-Grained Optimal 3D Face Dense Registration: An Iterative Dividing and Diffusing Method".INTERNATIONAL JOURNAL OF COMPUTER VISION (2023):21.