Style-based inverse kinematics

This paper presents an inverse kinematics system based on a learned model of human poses. Given a set of constraints, our system can produce the most likely pose satisfying those constraints, in real-time. Training the model on different input data leads to different styles of IK. The model is represented as a probability distribution over the space of all possible poses. This means that our IK system can generate any pose, but prefers poses that are most similar to the space of poses in the training data. We represent the probability with a novel model called a Scaled Gaussian Process Latent Variable Model. The parameters of the model are all learned automatically; no manual tuning is required for the learning component of the system. We additionally describe a novel procedure for interpolating between styles.Our style-based IK can replace conventional IK, wherever it is used in computer animation and computer vision. We demonstrate our system in the context of a number of applications: interactive character posing, trajectory keyframing, real-time motion capture with missing markers, and posing from a 2D image.

[1]  A. O'Hagan,et al.  Curve Fitting and Optimal Design for Prediction , 1978 .

[2]  R. Redner,et al.  Mixture densities, maximum likelihood, and the EM algorithm , 1984 .

[3]  Anthony A. Maciejewski,et al.  Computational modeling for the computer animation of legged figures , 1985, SIGGRAPH.

[4]  Chris Welman,et al.  INVERSE KINEMATICS AND GEOMETRIC CONSTRAINTS FOR ARTICULATED FIGURE MANIPULATION , 1993 .

[5]  Carl E. Rasmussen,et al.  In Advances in Neural Information Processing Systems , 2011 .

[6]  Zoran Popovic,et al.  Motion warping , 1995, SIGGRAPH.

[7]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[8]  Lance Williams,et al.  Motion signal processing , 1995, SIGGRAPH.

[9]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[10]  Stephen H. Lane,et al.  Synergy-based learning of hybrid position/force control for redundant manipulators , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[11]  J. Hahn,et al.  Interpolation Synthesis of Articulated Figure Motion , 1997, IEEE Computer Graphics and Applications.

[12]  Bobby Bodenheimer,et al.  The Process of Motion Capture: Dealing with the Data , 1997, Computer Animation and Simulation.

[13]  D. Mackay,et al.  Introduction to Gaussian processes , 1998 .

[14]  Norman I. Badler,et al.  Gesticulation behaviors for virtual humans , 1998, Proceedings Pacific Graphics '98. Sixth Pacific Conference on Computer Graphics and Applications (Cat. No.98EX208).

[15]  Michael F. Cohen,et al.  Verbs and Adverbs: Multidimensional Motion Interpolation , 1998, IEEE Computer Graphics and Applications.

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

[17]  Matthew Brand,et al.  Shadow puppetry , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[18]  Aaron F. Bobick,et al.  Parametric Hidden Markov Models for Gesture Recognition , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  William T. Freeman,et al.  Bayesian Reconstruction of 3D Human Motion from Single-Camera Video , 1999, NIPS.

[20]  Camillo J. Taylor,et al.  Reconstruction of articulated objects from point correspondences in a single uncalibrated image , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

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

[22]  Andrew Witkin,et al.  Believable automatically synthesized motion by knowledge-enhanced motion transformation , 2000 .

[23]  Rómer Rosales,et al.  Learning Body Pose via Specialized Maps , 2001, NIPS.

[24]  Peter-Pike J. Sloan,et al.  Artist‐Directed Inverse‐Kinematics Using Radial Basis Function Interpolation , 2001, Comput. Graph. Forum.

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

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

[27]  Christoph Bregler,et al.  Motion capture assisted animation: texturing and synthesis , 2002, ACM Trans. Graph..

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

[29]  Okan Arikan,et al.  Interactive motion generation from examples , 2002, ACM Trans. Graph..

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

[31]  David A. Forsyth,et al.  Motion synthesis from annotations , 2003, ACM Trans. Graph..

[32]  Carl E. Rasmussen,et al.  Warped Gaussian Processes , 2003, NIPS.

[33]  David A. Forsyth,et al.  Automatic Annotation of Everyday Movements , 2003, NIPS.

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

[35]  Katsu Yamane,et al.  Natural Motion Animation through Constraining and Deconstraining at Will , 2003, IEEE Trans. Vis. Comput. Graph..

[36]  Karan Singh,et al.  Eurographics/siggraph Symposium on Computer Animation (2003) Handrix: Animating the Human Hand , 2003 .

[37]  Trevor Darrell,et al.  Inferring 3D structure with a statistical image-based shape model , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[38]  Michael Gleicher,et al.  Automated extraction and parameterization of motions in large data sets , 2004, SIGGRAPH 2004.