We propose a new generative approach to physics-based motion capture.
Unlike prior attempts to incorporate physics into tracking, which 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.
This model facilitates estimation of body proportions and leverages
these in the physics and for improving fitting.
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 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.
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