Simulating Body Movements for Multiple Agent Avoidance

Animation of virtual characters in a crowd is one of the unavoidable hurdles artists and developers encounter throughout their work in different fields, such as cinematography, simulations, and game development. Depending on the requirements for the quality of motions, various techniques are used to overcome this constantly rising problem, each with its own shortcomings. Ignoring the interaction between individuals by using simple hitboxes to avoid collisions produces unrealistic movement patterns, while the manual animation of characters is a tedious and costly endeavor. This work discusses the implementation of an automated system capable of realistic human motion synthesis for multiple avatars, focusing on the interaction between individuals. The method is based on an autoregressive, data-driven conditional variational autoencoder and an additional control policy providing the latent input vector for the motion network trained with reinforcement learning. Different virtual sensor types that perceive a variable number of individuals and obstacles around the agent are proposed and evaluated. The system provides the simulation of the body movements in a multi-agent environment. It can operate at runtime with an adequate frame rate in environments containing more than 20 characters.