Rule-breaking behaviors, such as jaywalking or skipping queues, are common in crowds but difficult to study in real-world settings due to limited control and observability. Virtual reality (VR) provides a controlled alternative, but its validity depends on whether VR elicits realistic rule-breaking behavior. We conducted a VR study with 65 participants navigating a virtual city with four scenarios differing in social norms: walking on grass, crossing outside a crosswalk, jaywalking at a red light, and skipping a line. In each scenario, the proportion of rule-breaking virtual characters was manipulated (0%, 25%, 50%). Participants' movements and gaze were recorded to assess behavior and attention. Results showed higher rule-breaking as the number of violators increased, except in the low-stakes crossing scenario. Rule-breakers attended more to violating characters, whereas rule-followers focused on compliant ones. Participants cited efficiency, safety, and social norms as key factors guiding their decisions. Overall, VR reproduced natural patterns of social compliance and noncompliance, supporting its use for studying rule-breaking and applications in crowd simulation, urban design, safety training, and immersive media.

Improving motor performance is essential for sustaining long-term sports participation. In this study, we proposed a novel observational learning scheme for full-body sports movements, aiming to enhance its effectiveness by inducing a kinesthetic illusion. To implement this scheme, we developed a virtual reality (VR) system which can apply synchronized visuo-motor and visuo-tactile stimuli. In an experiment, participants who experienced the proposed scheme reported significantly higher perceived synchrony and subjectively rated their ball trajectory accuracy as improved after they performed actual golf driver swings. These findings suggest that our proposed observational learning scheme has the potential to support performance improvement in full-body sports movements.

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 behaviour 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 behaviours included in our model as intended. The results revealed significant differences in the participants’ perceptions of the various behaviours exhibited by our social crowd model.

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.