Title :
Learning Part-Based Models for Animation from Surface Motion Capture
Author :
Tejera, Margara ; Hilton, Adrian
Author_Institution :
Centre for Vision, Speech & Signal Process., Univ. of Surrey, Guildford, UK
fDate :
June 29 2013-July 1 2013
Abstract :
Surface motion capture (Surf Cap) enables 3D reconstruction of human performance with detailed cloth and hair deformation. However, there is a lack of tools that allow flexible editing of Surf Cap sequences. In this paper, we present a Laplacian editing technique that constrains the mesh deformation to plausible surface shapes learnt from a set of examples. A part-Based representation of the mesh enables learning of surface deformation locally in the space of Laplacian coordinates, avoiding correlations between body parts while preserving surface details. This extends the range of animation with natural surface deformation beyond the whole-body poses present in the Surf Cap data. We illustrate successful use of our tool on three different characters.
Keywords :
Laplace equations; computer animation; learning (artificial intelligence); mesh generation; solid modelling; 3D reconstruction; Laplacian coordinates; Laplacian editing technique; Surf Cap sequences; animation; cloth deformation; hair deformation; human performance; mesh deformation; mesh representation; natural surface deformation; part-based model learning; part-based representation; surface deformation learning; surface detail preservation; surface motion capture; surface shape learning; whole-body pose; Animation; Correlation; Deformable models; Interpolation; Laplace equations; Shape; Vectors; 3D video; Laplacian deformation; animation; part-Based models; surface performance capture;
Conference_Titel :
3D Vision - 3DV 2013, 2013 International Conference on
Conference_Location :
Seattle, WA
DOI :
10.1109/3DV.2013.29