Dynamical Simulation Priors for Human Motion Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
We propose a simulation-based dynamical motion prior for tracking human motion from video in presence of physical ground-person interactions. Most tracking approaches to date have focused on efficient inference algorithms and/or learning of prior kinematic motion models; however, few can explicitly account for physical plausibility of recovered motion. Here, we aim to recover physically plausible motion of a single articulated human subject. Towards this end, we propose a full-body 3D physical
... imulationbased prior that explicitly incorporates a model of human dynamics into the Bayesian filtering framework. We consider the motion of the subject to be generated by a feedback "control loop" in which Newtonian physics approximates the rigid-body motion dynamics of the human and the environment through the application and integration of interaction forces, motor forces and gravity. Interaction forces prevent physically impossible hypotheses, enable more appropriate reactions to the environment (e.g., ground contacts) and are produced from detected human-environment collisions. Motor forces actuate the body, ensure that proposed pose transitions are physically feasible and are generated using a motion controller. For efficient inference in the resulting high-dimensional state space, we utilize an exemplar-based control strategy that reduces the effective search space of motor forces. As a result, we are able to recover physically-plausible motion of human subjects from monocular and multi-view video. We show, both quantitatively and qualitatively, that our approach performs favorably with respect to Bayesian filtering methods with standard motion priors. Marek Vondrak was born in Prague, Czech Republic. He received his Sc.M. degree in computer science from Charles University, Prague and is currently pursuing a Ph.D. degree at Brown University, Providence, RI. Marek's research interests include recovery of articulated human motion from video, physical simulation, motion control of humanoids and character animation. His current major focus has concentrated on introducing techniques from computer graphics, robotics and animation to computer vision in order to build effective models of human motion for tracking.