Online motion synthesis using sequential Monte Carlo

We present a Model-Predictive Control (MPC) system for online synthesis of interactive and physically valid character motion. Our system enables a complex (36-DOF) 3D human character model to balance in a given pose, dodge projectiles, and improvise a get up strategy if forced to lose balance, all in a dynamic and unpredictable environment. Such contact-rich, predictive and reactive motions have previously only been generated offline or using a handcrafted state machine or a dataset of reference motions, which our system does not require. For each animation frame, our system generates trajectories of character control parameters for the near future --- a few seconds --- using Sequential Monte Carlo sampling. Our main technical contribution is a multimodal, tree-based sampler that simultaneously explores multiple different near-term control strategies represented as parameter splines. The strategies represented by each sample are evaluated in parallel using a causal physics engine. The best strategy, as determined by an objective function measuring goal achievement, fluidity of motion, etc., is used as the control signal for the current frame, but maintaining multiple hypotheses is crucial for adapting to dynamically changing environments.

[1]  James T. Kajiya,et al.  The rendering equation , 1986, SIGGRAPH.

[2]  John Lasseter,et al.  Principles of traditional animation applied to 3D computer animation , 1987, SIGGRAPH.

[3]  Andrew P. Witkin,et al.  Spacetime constraints , 1988, SIGGRAPH.

[4]  Michael F. Cohen,et al.  Interactive spacetime control for animation , 1992, SIGGRAPH.

[5]  Joe Marks,et al.  Spacetime constraints revisited , 1993, SIGGRAPH.

[6]  Karl Sims,et al.  Evolving virtual creatures , 1994, SIGGRAPH.

[7]  Wolfram Burgard,et al.  Monte Carlo Localization with Mixture Proposal Distribution , 2000, AAAI/IAAI.

[8]  Andrew Blake,et al.  Articulated body motion capture by annealed particle filtering , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[9]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[10]  Phil Husbands,et al.  Evolution of central pattern generators for bipedal walking in a real-time physics environment , 2002, IEEE Trans. Evol. Comput..

[11]  Nancy S. Pollard,et al.  Efficient synthesis of physically valid human motion , 2003, ACM Trans. Graph..

[12]  William T. Freeman,et al.  Efficient Multiscale Sampling from Products of Gaussian Mixtures , 2003, NIPS.

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

[14]  John Hart,et al.  ACM Transactions on Graphics , 2004, SIGGRAPH 2004.

[15]  Jessica K. Hodgins,et al.  Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces , 2004, SIGGRAPH 2004.

[16]  Jannik Fritsch,et al.  Kernel particle filter for real-time 3D body tracking in monocular color images , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[17]  Timo Aila,et al.  Mutated Kd-tree Importance Sampling , 2006 .

[18]  Kristine L. Bell,et al.  A Tutorial on Particle Filters for Online Nonlinear/NonGaussian Bayesian Tracking , 2007 .

[19]  Michael Gleicher,et al.  Parametric motion graphs , 2007, SI3D.

[20]  A. Doucet,et al.  A Tutorial on Particle Filtering and Smoothing: Fifteen years later , 2008 .

[21]  Jovan Popovic,et al.  Simulation of Human Motion Data using Short‐Horizon Model‐Predictive Control , 2008, Comput. Graph. Forum.

[22]  Zoran Popovic,et al.  Optimal gait and form for animal locomotion , 2009, ACM Trans. Graph..

[23]  J. Maciejowski,et al.  Sequential Monte Carlo for Model Predictive Control , 2009 .

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

[25]  David G. Lowe,et al.  Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration , 2009, VISAPP.

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

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

[28]  Mark H. Overmars,et al.  Real Time Animation of Virtual Humans: A Trade‐off Between Naturalness and Control , 2010, Comput. Graph. Forum.

[29]  M. V. D. Panne,et al.  Sampling-based contact-rich motion control , 2010, ACM Trans. Graph..

[30]  Igor S. Pandzic,et al.  State of the Art in Example‐Based Motion Synthesis for Virtual Characters in Interactive Applications , 2010, Comput. Graph. Forum.

[31]  Mark H. Overmars,et al.  Interactive Character Animation using Simulated Physics , 2011, Eurographics.

[32]  Adrien Bousseau,et al.  Real-time rough refraction , 2011, SI3D.

[33]  Jan Hauth,et al.  PF-MPC: Particle filter-model predictive control , 2011, Syst. Control. Lett..

[34]  Sumeetpal S. Singh,et al.  Particle predictive control , 2011 .

[35]  Yuval Tassa,et al.  Synthesis and stabilization of complex behaviors through online trajectory optimization , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[36]  Zoran Popovic,et al.  Discovery of complex behaviors through contact-invariant optimization , 2012, ACM Trans. Graph..

[37]  Aaron Hertzmann,et al.  Trajectory Optimization for Full-Body Movements with Complex Contacts , 2013, IEEE Transactions on Visualization and Computer Graphics.

[38]  Michiel van de Panne,et al.  Flexible muscle-based locomotion for bipedal creatures , 2013, ACM Trans. Graph..

[39]  Yuval Tassa,et al.  An integrated system for real-time model predictive control of humanoid robots , 2013, 2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids).