EVA: Generating Emotional Behavior of Virtual Agents using Expressive Features of Gait and Gaze
This work addresses the need for more emotionally expressive virtual agents in multi-agent VR environments, though it appears incremental as it builds on existing data-driven mappings for gait and gaze.
The paper tackles the problem of generating virtual agents with perceived emotions by using expressive features of gait and gaze, resulting in a real-time algorithm that can simulate hundreds of agents and increase the sense of presence in VR simulations.
We present a novel, real-time algorithm, EVA, for generating virtual agents with various perceived emotions. Our approach is based on using Expressive Features of gaze and gait to convey emotions corresponding to happy, sad, angry, or neutral. We precompute a data-driven mapping between gaits and their perceived emotions. EVA uses this gait emotion association at runtime to generate appropriate walking styles in terms of gaits and gaze. Using the EVA algorithm, we can simulate gaits and gazing behaviors of hundreds of virtual agents in real-time with known emotional characteristics. We have evaluated the benefits in different multi-agent VR simulation environments. Our studies suggest that the use of expressive features corresponding to gait and gaze can considerably increase the sense of presence in scenarios with multiple virtual agents.