F2FCrowds: Planning Agent Movements to Enable Face-to-Face Interactions

We present an approach for multi-agent navigation that facilitates face-to-face interaction in virtual crowds. We describe a model of approach behavior for virtual agents that includes a novel interaction velocity prediction (IVP) algorithm. This algorithm is combined with human body motion synthesis constraints and facial actions to improve the behavioral realism of virtual agents. We combine these techniques with full-body crowd simulation and evaluate their benefits by conducting a user study using immersive hardware. Our results indicate that such techniques enabling face-to-face interactions can improve the sense of presence felt by the user. The virtual agents using these algorithms also appear more responsive and are able to elicit more reaction from the users. F2FCrowds: Planning Agent Movements to Enable Face-to-Face Interactions 2 Figure 1: F2FCrowds: Our algorithm enables the real user (in blue, wearing an HMD) to have face-to-face interactions with virtual agents. The virtual agents are responsive and exhibit head movements, gazing and gesture behaviors.