Detail-preserving controllable deformation from sparse examples

Recent advances in laser scanning technology have made it possible to faithfully scan a real object with tiny geometric details, such as pores and wrinkles. However, a faithful digital model should not only capture static details of the real counterpart but also be able to reproduce the deformed ver...

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Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1996. - 18(2012), 8 vom: 03. Aug., Seite 1215-27
1. Verfasser: Huang, Haoda (VerfasserIn)
Weitere Verfasser: Yin, KangKang, Zhao, Ling, Qi, Yue, Yu, Yizhou, Tong, Xin
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2012
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.
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520 |a Recent advances in laser scanning technology have made it possible to faithfully scan a real object with tiny geometric details, such as pores and wrinkles. However, a faithful digital model should not only capture static details of the real counterpart but also be able to reproduce the deformed versions of such details. In this paper, we develop a data-driven model that has two components; the first accommodates smooth large-scale deformations and the second captures high-resolution details. Large-scale deformations are based on a nonlinear mapping between sparse control points and bone transformations. A global mapping, however, would fail to synthesize realistic geometries from sparse examples, for highly deformable models with a large range of motion. The key is to train a collection of mappings defined over regions locally in both the geometry and the pose space. Deformable fine-scale details are generated from a second nonlinear mapping between the control points and per-vertex displacements. We apply our modeling scheme to scanned human hand models, scanned face models, face models reconstructed from multiview video sequences, and manually constructed dinosaur models. Experiments show that our deformation models, learned from extremely sparse training data, are effective and robust in synthesizing highly deformable models with rich fine features, for keyframe animation as well as performance-driven animation. We also compare our results with those obtained by alternative techniques 
650 4 |a Journal Article 
650 4 |a Research Support, Non-U.S. Gov't 
650 4 |a Research Support, U.S. Gov't, Non-P.H.S. 
700 1 |a Yin, KangKang  |e verfasserin  |4 aut 
700 1 |a Zhao, Ling  |e verfasserin  |4 aut 
700 1 |a Qi, Yue  |e verfasserin  |4 aut 
700 1 |a Yu, Yizhou  |e verfasserin  |4 aut 
700 1 |a Tong, Xin  |e verfasserin  |4 aut 
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