Social Crowd Simulation: Improving Realism with Social Rules and Gaze Behavior

This paper proposes a novel approach to enhance the realism of a rule-based crowd simulation model by incorporating social rules and gaze-driven attention with consistent animations. Current crowd simulation models focus mostly on steering towards a goal while avoiding collision based on the agent’s direction of movement. This leads to robot-like simulations since agents’ appear to always have their attention perfectly aligned with the direction of movement. In the real world we observe that humans move in a crowd performing collision avoidance driven by attention, gaze, and non-verbal coordination with incoming traffic. In addition, humans exhibit different steering strategies based on whether they walk alone or in a group, whether they can look ahead and plan their best local movement, or react more abruptly because their gaze diverts from their direction of movement. Human gaze can be driven by movement but also by distractions such as being engaged in conversation with other people or using mobile phones. These human features are overlooked in crowd simulation leading often to perfectly smooth local movements of individuals. Our proposed method enhances existing crowd simulation frameworks by integrating social behavior models that affect both individual and collective dynamics, and drives gaze behavior to better simulate attention. By applying these models at both the steering and animation levels, we significantly improve the realism of the crowd simulation. A user study shows that our model was perceived as being more realistic and consistent with social behaviors when compared to a more traditional collision avoidance with just locomotion or with random animations.