PMP: Learning to Physically Interact with Environments using Part-wise
  Motion Priors
Jinseok Bae Jungdam Won Donggeun Lim Cheol-Hui Min Young Min Kim
We present a method to animate a character incorporating multiple part-wise
motion priors (PMP). While previous works allow creating realistic articulated
motions from reference data, the range of motion is largely limited by the
available samples. Especially for the interaction-rich scenarios, it is
impractical to attempt acquiring every possible interacting motion, as the
combination of physical parameters increases exponentially. The proposed PMP
allows us to assemble multiple part skills to animate a character, creating a
diverse set of motions with different combinations of existing data. In our
pipeline, we can train an agent with a wide range of part-wise priors.
Therefore, each body part can obtain a kinematic insight of the style from the
motion captures, or at the same time extract dynamics-related information from
the additional part-specific simulation. For example, we can first train a
general interaction skill, e.g. grasping, only for the dexterous part, and then
combine the expert trajectories from the pre-trained agent with the kinematic
priors of other limbs. Eventually, our whole-body agent learns a novel physical
interaction skill even with the absence of the object trajectories in the
reference motion sequence.