Social crowd simulation: Improving realism with social rules and gaze behavior

Current crowd simulation models focus mostly on steering towards a goal while avoiding collisions 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, often leading to perfectly smooth local movements of individuals. Unfortunately, this lack of social behavior and variety in animations may be perceived as unrealistic when observing the results on a 2D display, and it may become even more apparent in immersive scenarios where the participant is at eye level with the virtual humans. 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. The ultimate goal is to make immersive virtual crowds more realistic. 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. We conducted validation user studies on both a 2D display and in immersive VR, and observed that applying these models to both the steering and animation levels significantly improves the realism of the crowd simulation. The 2D display based user study based on video comparisons showed that our model was perceived as more realistic and consistent with social behaviors compared to traditional collision avoidance approaches, that used only locomotion or random animations. The immersive user study showed that participants effectively detected the social behaviors included in our model as intended. The results revealed significant differences in the participants’ perceptions of the various behaviors exhibited by our social crowd model.