Physical simulation for probabilistic motion tracking. CVPR

Human motion tracking is an important problem in com-puter vision. Most prior approaches have concentrated on efficient inference algorithms and prior motion models; however, few can explicitly account for physical plausibility of recovered motion. The primary purpose of this work is to enforce physical plausibility in the tracking of a single artic-ulated human subject. Towards this end, we propose a full-body 3D physical simulation-based prior that explicitly in-corporates motion control and dynamics into the Bayesian filtering framework. We consider the human’s motion to be generated by a “control loop”. In this control loop, Newto-nian physics approximates the rigid-body motion dynamics of the human and the environment through the application and integration of forces. Collisions generate interaction forces to prevent physically impossible hypotheses. This al-lows us to properly model human motion dynamics, ground contact and environment interactions. For efficient infer-ence in the resulting high-dimensional state space, we in-troduce exemplar-based control strategy to reduce the ef-fective search space. As a result we are able to recover the physically-plausible kinematic and dynamic state of the body from monocular and multi-view imagery. We show, both quantitatively and qualitatively, that our approach per-forms favorably with respect to standard Bayesian filtering methods. 1