Boosting Local Shape Matching for Dense 3D Face Correspondence | |
Fan, Zhenfeng1,2; Hu, Xiyuan,1,2; Chen, Chen1,2; Peng, Silong1,2,3 | |
2019-06 | |
会议名称 | IEEE Conference on Computer Vision and Pattern Recognition |
页码 | 10944-10954 |
会议日期 | JUN 16-20, 2019 |
会议地点 | Long Beach, CA |
出版者 | IEEE |
产权排序 | 1 |
摘要 | Dense 3D face correspondence is a fundamental and challenging issue in the literature of 3D face analysis. Correspondence between two 3D faces can be viewed as a nonrigid registration problem that one deforms into the other, which is commonly guided by a few facial landmarks in many existing works. However, the current works seldom consider the problem of incoherent deformation caused by landmarks. In this paper, we explicitly formulate the deformation as locally rigid motions guided by some seed points, and the formulated deformation satisfies coherent local motions everywhere on a face. The seed points are initialized by a few landmarks, and are then augmented to boost shape matching between the template and the target face step by step, to finally achieve dense correspondence. In each step, we employ a hierarchical scheme for local shape registration, together with a Gaussian reweighting strategy for accurate matching of local features around the seed points. In our experiments, we evaluate the proposed method extensively on several datasets, including two publicly available ones: FRGC v2.0 and BU-3DFE. The experimental results demonstrate that our method can achieve accurate feature correspondence, coherent local shape motion, and compact data representation. These merits actually settle some important issues for practical applications, such as expressions, noise, and partial data. |
关键词 | 3D Face Dense correspondence |
学科领域 | 人工智能 |
DOI | 10.1109/CVPR.2019.01120 |
URL | 查看原文 |
收录类别 | EI |
资助项目 | National Key R&D Program of China[2017YFC0803505] ; Open Project of National Engineering Laboratory for Forensic Science of China[2017NELKFKT02] ; National Key R&D Program of China[2017YFC0803505] ; Open Project of National Engineering Laboratory for Forensic Science of China[2017NELKFKT02] |
语种 | 英语 |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/26222 |
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 | Hu, Xiyuan, |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.Beijing Visytem Co. Ltd. |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Fan, Zhenfeng,Hu, Xiyuan,,Chen, Chen,et al. Boosting Local Shape Matching for Dense 3D Face Correspondence[C]:IEEE,2019:10944-10954. |
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