From motion capture to action capture: a review of imitation learning techniques and their application to VR-based character animation

We present a novel method for virtual character animation that we call action capture. In this approach, virtual characters learn to imitate the actions of Virtual Reality (VR) users by tracking not only the users' movements but also their interactions with scene objects.Action capture builds on conventional motion capture but differs from it in that higher-level action representations are transferred rather than low-level motion data. As an advantage, the learned actions can often be naturally applied to varying situations, thus avoiding retargetting problems of motion capture. The idea of action capture is inspired by human imitation learning; related methods have been investigated for a longer time in robotics. The paper reviews the relevant literature in these areas before framing the concept of action capture in the context of VR-based character animation. We also present an example in which the actions of a VR user are transferred to a virtual worked.