A Statistical Model for Coupled Human Shape and Motion Synthesis

Due to rapid development of virtual reality industry, realistic modeling and animation is becoming more and more important. In the paper, we propose a method to synthesize both human appearance and motion given semantic parameters, as well as to create realistic animation of still meshes and to synthesize appearance based on a given motion. Our approach is data-driven and allows to correlate two databases containing shape and motion data. The synthetic output of the model is evaluated quantitatively and in terms of visual plausibility.

[1]  Ashley Reid,et al.  A comparison of linear skinning techniques for character animation , 2007, AFRIGRAPH '07.

[2]  Jehee Lee,et al.  Simulating biped behaviors from human motion data , 2007, ACM Trans. Graph..

[3]  Dinesh Manocha,et al.  PLEdestrians: a least-effort approach to crowd simulation , 2010, SCA '10.

[4]  Jovan Popovic,et al.  Guided time warping for motion editing , 2007, SCA '07.

[5]  Jessica K. Hodgins,et al.  Perception of Human Motion With Different Geometric Models , 1998, IEEE Trans. Vis. Comput. Graph..

[6]  Jovan Popovic,et al.  Automatic rigging and animation of 3D characters , 2007, ACM Trans. Graph..

[7]  Nancy S. Pollard,et al.  Perceptual metrics for character animation: sensitivity to errors in ballistic motion , 2003, ACM Trans. Graph..

[8]  C. Karen Liu,et al.  Learning physics-based motion style with nonlinear inverse optimization , 2005, ACM Trans. Graph..

[9]  Aaron Hertzmann,et al.  Style machines , 2000, SIGGRAPH 2000.

[10]  Sebastian Thrun,et al.  SCAPE: shape completion and animation of people , 2005, SIGGRAPH '05.

[11]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[12]  In-Kwon Lee,et al.  Enriching a motion database by analogous combination of partial human motions , 2007, The Visual Computer.

[13]  David J. Fleet,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Gaussian Process Dynamical Model , 2007 .

[14]  Carol O'Sullivan,et al.  Clone attack! Perception of crowd variety , 2008, ACM Trans. Graph..

[15]  R. Fisher.  The Advanced Theory of Statistics , 1943, Nature.

[16]  Carol O'Sullivan,et al.  Perceiving human motion variety , 2011, APGV '11.

[17]  Hans-Peter Seidel,et al.  A Statistical Model of Human Pose and Body Shape , 2009, Comput. Graph. Forum.

[18]  James M. Rehg,et al.  A data-driven approach to quantifying natural human motion , 2005, SIGGRAPH '05.

[19]  N. Troje.  Decomposing biological motion: a framework for analysis and synthesis of human gait patterns. , 2002, Journal of vision.

[20]  Ming C. Lin,et al.  Aggregate dynamics for dense crowd simulation , 2009, ACM Trans. Graph..

[21]  Maurice G. Kendall,et al.  The advanced theory of statistics , 1945 .

[22]  Zoran Popovic,et al.  Compact character controllers , 2009, ACM Trans. Graph..

[23]  Zoran Popovic,et al.  Physically based motion transformation , 1999, SIGGRAPH.

[24]  Amr Adel Hassan Ahmed,et al.  Parametric synthesis of human animation , 2004 .

[25]  Harry Shum,et al.  Motion texture: a two-level statistical model for character motion synthesis , 2002, ACM Trans. Graph..

[26]  Marco da Silva,et al.  Interactive simulation of stylized human locomotion , 2008, ACM Trans. Graph..

[27]  Michael J. Black,et al.  Implicit Probabilistic Models of Human Motion for Synthesis and Tracking , 2002, ECCV.