  Simulating realistic interaction and motions for physics-based characters is
of great interest for interactive applications, and automatic secondary
character animation in the movie and video game industries. Recent works in
reinforcement learning have proposed impressive results for single character
simulation, especially the ones that use imitation learning based techniques.
However, imitating multiple characters interactions and motions requires to
also model their interactions. In this paper, we propose a novel Multi-Agent
Generative Adversarial Imitation Learning based approach that generalizes the
idea of motion imitation for one character to deal with both the interaction
and the motions of the multiple physics-based characters. Two unstructured
datasets are given as inputs: 1) a single-actor dataset containing motions of a
single actor performing a set of motions linked to a specific application, and
2) an interaction dataset containing a few examples of interactions between
multiple actors. Based on these datasets, our system trains control policies
allowing each character to imitate the interactive skills associated with each
actor, while preserving the intrinsic style. This approach has been tested on
two different fighting styles, boxing and full-body martial art, to demonstrate
the ability of the method to imitate different styles.
