caa a22 4500 605540217 CHVBK 20210128100910.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00371-013-0901-6 doi (NATIONALLICENCE)springer-10.1007/s00371-013-0901-6 Learning human shape model from multiple databases with correspondence considering kinematic consensus [Elektronische Daten] [Yao Yu, Yu Zhou, Sidan Du, Yuan Jie, Ziqiang Wang, Zhengyu Cai] In various applications of computer graphics and model-based computer vision, a human shape model cannot only model the kinematic properties of a subject to drive the mesh into various postures, but it can also be utilized to parameterize the shape variations across individuals. It is of great benefit to improve the diversity of the training databases by learning the model from multiple databases, once the correspondences among scans of these databases can be achieved. To accomplish this goal, we proposed a framework to match the scans from multiple databases, using the assistance of kinematic properties, to compute the correspondences. The resulting correspondence is accurate, robust, capable of handling scan incompletion, and is homogeneous across shapes and postures. In our approach, we start with evaluating how a correspondence, which is achieved via minimizing the deformation energy, agrees with the kinematic properties, and then, we jointly fit the source scans to the target scans to derive the correspondences between the databases. The extensive results show that our approach can generate a faithful correspondence even in extreme cases, without carefully selecting the deformation factors and markers. We also developed a method, with which a commendable and predictable result can be synthesized, to control the rendered shape in an intuitive way. Springer-Verlag Berlin Heidelberg, 2013 Deformations nationallicence Morphing nationallicence Non-rigid registration nationallicence Motion capture and synthesis nationallicence Correspondence nationallicence Yu Yao School of Electronic Science and Engineering, Nanjing University, 210046, Nanjing, Jiangsu, China aut Zhou Yu School of Electronic Science and Engineering, Nanjing University, 210046, Nanjing, Jiangsu, China aut Du Sidan School of Electronic Science and Engineering, Nanjing University, 210046, Nanjing, Jiangsu, China aut Jie Yuan School of Electronic Science and Engineering, Nanjing University, 210046, Nanjing, Jiangsu, China aut Wang Ziqiang School of Electronic Science and Engineering, Nanjing University, 210046, Nanjing, Jiangsu, China aut Cai Zhengyu School of Electronic Science and Engineering, Nanjing University, 210046, Nanjing, Jiangsu, China aut The Visual Computer Springer Berlin Heidelberg 31/1(2015-01-01), 19-33 0178-2789 31:1<19 2015 31 371 https://doi.org/10.1007/s00371-013-0901-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00371-013-0901-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Yu Yao School of Electronic Science and Engineering, Nanjing University, 210046, Nanjing, Jiangsu, China aut NATIONALLICENCE 700 1- Zhou Yu School of Electronic Science and Engineering, Nanjing University, 210046, Nanjing, Jiangsu, China aut NATIONALLICENCE 700 1- Du Sidan School of Electronic Science and Engineering, Nanjing University, 210046, Nanjing, Jiangsu, China aut NATIONALLICENCE 700 1- Jie Yuan School of Electronic Science and Engineering, Nanjing University, 210046, Nanjing, Jiangsu, China aut NATIONALLICENCE 700 1- Wang Ziqiang School of Electronic Science and Engineering, Nanjing University, 210046, Nanjing, Jiangsu, China aut NATIONALLICENCE 700 1- Cai Zhengyu School of Electronic Science and Engineering, Nanjing University, 210046, Nanjing, Jiangsu, China aut NATIONALLICENCE 773 0- The Visual Computer Springer Berlin Heidelberg 31/1(2015-01-01), 19-33 0178-2789 31:1<19 2015 31 371