Constraining Human Body Tracking

David Demirdjian, Teresa Ko, Trevor Darrell

Abstract: Our paper addresses the problem of enforcing constraints in human body tracking. A projection technique is derived to impose kinematic constraints on independent multibody motion: we show that for small motions the multibody articulated motion space can be approximated by a linear manifold estimated directly from the previous body pose. We propose a learning approach to model nonlinear constraints; we train a support vector classifier from motion capture data to model the boundary of the space of valid poses. Linear and nonlinear body pose constraints are enforced by first projecting unconstrained motions onto the articulated motion space and then optimizing to find points on this linear manifold that lie within the non-linear constraint surface modeled by the SVM classifier.