Synthesis of Responsive Motion Using a Dynamic Model

Synthesizing the movements of a responsive virtual character in the event of unexpected perturbations has proven a difficult challenge. To solve this problem, we devise a fully automatic method that learns a nonlinear probabilistic model of dynamic responses from very few perturbed walking sequences. This model is able to synthesize responses and recovery motions under new perturbations different from those in the training examples. When perturbations occur, we propose a physics‐based method that initiates motion transitions to the most probable response example based on the dynamic states of the character. Our algorithm can be applied to any motion sequences without the need for preprocessing such as segmentation or alignment. The results show that three perturbed motion clips can sufficiently generate a variety of realistic responses, and 14 clips can create a responsive virtual character that reacts realistically to external forces in different directions applied on different body parts at different moments in time.

[1]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

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

[3]  Petros Faloutsos,et al.  Composable controllers for physics-based character animation , 2001, SIGGRAPH.

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

[5]  Jessica K. Hodgins,et al.  Interactive control of avatars animated with human motion data , 2002, SIGGRAPH.

[6]  Michael A. Saunders,et al.  SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization , 2002, SIAM J. Optim..

[7]  Jessica K. Hodgins,et al.  Motion capture-driven simulations that hit and react , 2002, SCA '02.

[8]  Neil D. Lawrence,et al.  Fast Sparse Gaussian Process Methods: The Informative Vector Machine , 2002, NIPS.

[9]  Frédéric H. Pighin,et al.  Hybrid control for interactive character animation , 2003, 11th Pacific Conference onComputer Graphics and Applications, 2003. Proceedings..

[10]  Neil D. Lawrence,et al.  Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data , 2003, NIPS.

[11]  Dinesh K. Pai,et al.  Motion perturbation based on simple neuromotor control models , 2003, 11th Pacific Conference onComputer Graphics and Applications, 2003. Proceedings..

[12]  Jessica K. Hodgins,et al.  Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces , 2004, ACM Trans. Graph..

[13]  Aaron Hertzmann,et al.  Style-based inverse kinematics , 2004, SIGGRAPH 2004.

[14]  Aaron Hertzmann,et al.  Style-based inverse kinematics , 2004, ACM Trans. Graph..

[15]  Dinesh K. Pai,et al.  Data-driven Interactive Balancing Behaviors , 2005 .

[16]  Victor B. Zordan,et al.  Dynamic response for motion capture animation , 2005, SIGGRAPH 2005.

[17]  David J. Fleet,et al.  Priors for people tracking from small training sets , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[18]  David J. Fleet,et al.  Gaussian Process Dynamical Models , 2005, NIPS.

[19]  David A. Forsyth,et al.  Pushing people around , 2005, SCA '05.

[20]  Zoubin Ghahramani,et al.  Sparse Gaussian Processes using Pseudo-inputs , 2005, NIPS.

[21]  Victor B. Zordan,et al.  Physically based grasping control from example , 2005, SCA '05.

[22]  Victor B. Zordan,et al.  Dynamic response for motion capture animation , 2005, SIGGRAPH '05.

[23]  Joaquin Quiñonero Candela,et al.  Local distance preservation in the GP-LVM through back constraints , 2006, ICML.

[24]  Z. Popovic,et al.  Model reduction for real-time fluids , 2006, SIGGRAPH 2006.

[25]  D. Lawrence.  The Gaussian Process Latent Variable Model , 2006 .

[26]  David J. Fleet,et al.  3D People Tracking with Gaussian Process Dynamical Models , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[27]  Adrien Treuille,et al.  Model reduction for real-time fluids , 2006, ACM Trans. Graph..

[28]  KangKang Yin,et al.  SIMBICON: simple biped locomotion control , 2007, ACM Trans. Graph..

[29]  Jehee Lee,et al.  Simulating biped behaviors from human motion data , 2007, SIGGRAPH 2007.

[30]  Jovan Popovic,et al.  Multiobjective control with frictional contacts , 2007, SCA '07.

[31]  Kwang Won Sok,et al.  Simulating biped behaviors from human motion data , 2007, ACM Trans. Graph..

[32]  Neil D. Lawrence,et al.  Hierarchical Gaussian process latent variable models , 2007, ICML '07.

[33]  Jessica K. Hodgins,et al.  Constraint-based motion optimization using a statistical dynamic model , 2007, ACM Trans. Graph..

[34]  C. Karen Liu,et al.  Animating responsive characters with dynamic constraints in near-unactuated coordinates , 2008, ACM Trans. Graph..

[35]  Philippe Beaudoin,et al.  Continuation methods for adapting simulated skills , 2008, ACM Trans. Graph..

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

[37]  Jernej Barbic,et al.  Real-time control of physically based simulations using gentle forces , 2008, ACM Trans. Graph..

[38]  C. Karen Liu,et al.  Animating responsive characters with dynamic constraints in near-unactuated coordinates , 2008, SIGGRAPH 2008.

[39]  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 .

[40]  Christos Faloutsos,et al.  Laziness is a Virtue: Motion Stitching Using Effort Minimization , 2008, Eurographics.

[41]  Lucas Kovar,et al.  Motion graphs , 2002, SIGGRAPH '08.

[42]  Philippe Beaudoin,et al.  Synthesis of constrained walking skills , 2008, SIGGRAPH Asia '08.

[43]  Lucas Kovar,et al.  Motion graphs , 2002, SIGGRAPH Classes.

[44]  David J. Fleet,et al.  Optimizing walking controllers , 2009, SIGGRAPH 2009.

[45]  Philippe Beaudoin,et al.  Robust task-based control policies for physics-based characters , 2009, ACM Trans. Graph..

[46]  C. Karen Liu,et al.  Optimization-based interactive motion synthesis , 2009, ACM Trans. Graph..

[47]  David J. Fleet,et al.  Optimizing walking controllers , 2009, ACM Trans. Graph..

[48]  David A. Forsyth,et al.  Generalizing motion edits with Gaussian processes , 2009, ACM Trans. Graph..

[49]  Victor B. Zordan,et al.  Momentum control for balance , 2009, ACM Trans. Graph..

[50]  Frédo Durand,et al.  Linear Bellman combination for control of character animation , 2009, ACM Trans. Graph..

[51]  Zoran Popovic,et al.  Contact-aware nonlinear control of dynamic characters , 2009, ACM Trans. Graph..

[52]  Jessica K. Hodgins,et al.  Simulating balance recovery responses to trips based on biomechanical principles , 2009, SCA '09.

[53]  Zhigang Deng,et al.  Style learning and transferring for facial animation editing , 2009, SCA '09.

[54]  Ziv Bar-Joseph,et al.  Modeling spatial and temporal variation in motion data , 2009, ACM Trans. Graph..