Evaluating Realism for Virtual Supporting Characters

Convincing and compelling virtual environments that are populated with rich characters demand consistent, nuanced, and realistic behavior that is integrated in the surrounding environment. Inadequate and unscalable traditional Artificial Intelligence based approaches have made it impractical to apply this level of character depth and detail to large environments. We introduce the concept of Supporting Character Realism (SCR) for virtual avatars by identifying the capabilities of agents which have the ability to work in tandem with traditional “main character realism” approaches by demonstrating consistent and nuanced behaviors that blend into the surrounding background environment. Next, we propose several metrics for evaluating agents attempting to achieve this level of realism and test our proposed metrics in a rich social interaction experiment placed in a virtual bar amidst a variety of human and computer controlled patrons. Finally, we gauge the performance of a set of both traditional scripted bots and prototype AI-driven agents designed to target our concept of SCR. Our results show that SCR is not only a distinguishable and measurable metric of agent realism, but also a technically achievable goal within the reach of modern AI techniques.