  Existing deep models predict 2D and 3D kinematic poses from video that are
approximately accurate, but contain visible errors that violate physical
constraints, such as feet penetrating the ground and bodies leaning at extreme
angles. In this paper, we present a physics-based method for inferring 3D human
motion from video sequences that takes initial 2D and 3D pose estimates as
input. We first estimate ground contact timings with a novel prediction network
which is trained without hand-labeled data. A physics-based trajectory
optimization then solves for a physically-plausible motion, based on the
inputs. We show this process produces motions that are significantly more
realistic than those from purely kinematic methods, substantially improving
quantitative measures of both kinematic and dynamic plausibility. We
demonstrate our method on character animation and pose estimation tasks on
dynamic motions of dancing and sports with complex contact patterns.
