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Key‐styling: learning motion style for real‐time synthesis of 3D animation
Author(s) -
Wang Yi,
Liu ZhiQiang,
Zhou LiZhu
Publication year - 2006
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.126
Subject(s) - computer science , animation , key (lock) , character animation , motion (physics) , artificial intelligence , style (visual arts) , motion capture , variable (mathematics) , sequence (biology) , computer animation , computer graphics (images) , computer vision , human–computer interaction , mathematical analysis , genetics , computer security , mathematics , archaeology , biology , history
In this paper, we present a novel real‐time motion synthesis approach that can generate 3D character animation with required style. The effectiveness of our approach comes from learning captured 3D human motion as a self‐organizing mixture network (SOMN); of parametric Gaussians.The learned model describes the motion under the control of a vector variable called style variable , and acts as a probabilistic mapping from the low‐dimensional style values to the high‐dimensional 3D poses. We design a pose synthesis algorithm to allow the user to generate poses by specifying new style values. We also propose a novel motion synthesis method, the key‐styling, which accepts a sparse sequence of key style values and interpolates a dense sequence of style values to synthesize an animation. Key‐styling is able to produce animations that are more realistic and natural‐looking than those synthesized with the traditional key‐keyframing technique. Copyright © 2006 John Wiley & Sons, Ltd.