Simulating gaze attention behaviors for crowds

Crowd animation is a topic of high interest which offers many challenges. One of the most important is the trade-off between rich, realistic behaviors and computational costs. To this end, much effort has been put into creating variety in character representation and animation. Nevertheless, one aspect still lacking realism in virtual crowd characters resides in their attention behaviors. In this paper, we propose a framework to add gaze attention behaviors to crowd animations. First, we automatically extract interest points from character or object trajectories in pre-existing animations. For a given character, we assign a set of elementary scores based on parameters such as dis-