Decision Mechanisms for Interactive Character Animations in Virtual Environment

Abstract

Recently, interactive character animations in computer games are mainly rely on motion-captured or carefully crafted motion clips. However, it is impractical and difficult to provide motion data samples for all possible behaviors with creating realistic responses to unexpected changes in the world. In order to control characters animations more precisely and realistically in real-time, an decision mechanism that synthesizes animations with interaction between characters in virtual environment is necessary. An efficient near-optimal interactive motion controller that can intelligently adopt the input motion data to a dynamically changing virtual environment is present. The controllers using reinforcement learning to reduce the data requirement by finding the most effective set of motion data to create the desired behaviors, and can be used as basic components in global path planning, and it further reduces the computational processing burden in the real-time interactive applications.