  While current methods have shown promising progress on estimating 3D human
motion from monocular videos, their motion estimates are often physically
unrealistic because they mainly consider kinematics. In this paper, we
introduce Physics-aware Pretrained Transformer (PhysPT), which improves
kinematics-based motion estimates and infers motion forces. PhysPT exploits a
Transformer encoder-decoder backbone to effectively learn human dynamics in a
self-supervised manner. Moreover, it incorporates physics principles governing
human motion. Specifically, we build a physics-based body representation and
contact force model. We leverage them to impose novel physics-inspired training
losses (i.e., force loss, contact loss, and Euler-Lagrange loss), enabling
PhysPT to capture physical properties of the human body and the forces it
experiences. Experiments demonstrate that, once trained, PhysPT can be directly
applied to kinematics-based estimates to significantly enhance their physical
plausibility and generate favourable motion forces. Furthermore, we show that
these physically meaningful quantities translate into improved accuracy of an
important downstream task: human action recognition.
