  We propose a generative approach to physics-based motion capture. Unlike
prior attempts to incorporate physics into tracking that assume the subject and
scene geometry are calibrated and known a priori, our approach is automatic and
online. This distinction is important since calibration of the environment is
often difficult, especially for motions with props, uneven surfaces, or outdoor
scenes. The use of physics in this context provides a natural framework to
reason about contact and the plausibility of recovered motions. We propose a
fast data-driven parametric body model, based on linear-blend skinning, which
decouples deformations due to pose, anthropometrics and body shape. Pose (and
shape) parameters are estimated using robust ICP optimization with
physics-based dynamic priors that incorporate contact. Contact is estimated
from torque trajectories and predictions of which contact points were active.
To our knowledge, this is the first approach to take physics into account
without explicit {\em a priori} knowledge of the environment or body
dimensions. We demonstrate effective tracking from a noisy single depth camera,
improving on state-of-the-art results quantitatively and producing better
qualitative results, reducing visual artifacts like foot-skate and jitter.
