Kexiang Huang

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 VRelicits 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.

We present a reinforcement learning framework for crowd simulation that balances collision avoidance with navigation preferences guided by soft environmental constraints. Our approach integrates two complementary perception components: entity perception, which handles hard constraints imposed by physical obstacles, and implicit environmental perception, encoded as suitability maps that guide movement preferences due to soft constraints. To ensure robust generalization across complex scenes, we employ a modular, two-phase training strategy utilizing curriculum-based environmental templates. The proposed framework functions as an intuitive crowd authoring tool: artists can influence crowd behavior in real time by interactively editing suitability maps or dynamically adjusting perception weights, either globally or per-agent. This enables adaptive navigation behaviors, such as dynamic trajectory prioritization based on the environment, to emerge from local interactions. We validate our approach using both quantitative metrics and qualitative analysis, including a user study confirming that the resulting behaviors align with pedestrian traffic patterns observed in realworld settings. We further present an example of emergent social behavior through the formation and gradual evolution of desire paths. This work contributes to the field of crowd simulation by offering a robust, learning-based framework that supports heterogeneous navigation modeling and intuitive authoring for interactive applications.

We present a reinforcement learning framework for crowd simulation that balances collision avoidance with navigation preferences guided by soft environmental constraints. Our approach integrates two complementary perception components: entity perception, which handles hard constraints imposed by physical obstacles, and implicit environmental perception, encoded as suitability maps that guide movement preferences due to soft constraints. To ensure robust generalization across complex scenes, we employ a modular, two-phase training strategy utilizing curriculum-based environmental templates. The proposed framework functions as an intuitive crowd authoring tool: artists can influence crowd behavior in real time by interactively editing suitability maps or dynamically adjusting perception weights, either globally or per-agent. This enables adaptive navigation behaviors, such as dynamic trajectory prioritization based on the environment, to emerge from local interactions. We validate our approach using both quantitative metrics and qualitative analysis, including a user study confirming that the resulting behaviors align with pedestrian traffic patterns observed in real-world settings. We further present an example of emergent social behavior through the formation and gradual evolution of desire paths. This work contributes to the field of crowd simulation by offering a robust, learning-based framework that supports heterogeneous navigation modeling and intuitive authoring for interactive applications.

The traditional social force model (SFM) in crowd simulation experiences difficulty coping with the complexity of the crowd, limited by singular physical formulas and parameters. Recent attempts to combine deep learning with these models focus more on simulating specific states of crowds. This paper introduces an advanced deep social force model, influenced by crowd states. It utilizes deep neural networks to accurately fit crowd trajectory features, enhancing behavior simulation capabilities. Geometrical constraints within the model provide control over varied crowd behaviors, adjustable to simulate different crowd types. Before training, we use the SFM to refine behaviors in real trajectories with excessively small distances, aiming to enhance the general applicability of the model. Comparative experiments affirm the effectiveness of the model, showing comparable performance to both classic physical models and modern learning-based hybrid models in pedestrian simulations, with reduced collisions. In addition, the model has a certain ability to simulate crowds with high density and diverse behaviors.

Achieving crowd formation transformations has wide‐ranging applications in fields such as unmanned aerial vehicle formation control, crowd simulation, and large‐scale performances. However, planning trajectories for hundreds of agents is a challenging and tedious task. When modifying crowd formation change schemes, adjustments are typically required based on the style of formation change. Existing methods often involve manual adjustments at each crucial step, leading to a substantial amount of physical labor. Motivated by these challenges, this study introduces a novel generative adversarial network (GAN) for generating crowd formation transformations. The proposed GAN learns specific styles from a series of crowd formation transformation trajectories and can transform a new crowd with an arbitrary number of individuals into the same styles with minimal manual intervention. The model incorporates a space–time transformer module to aggregate spatiotemporal information for learning distinct styles of formation transformation. Furthermore, this article investigates the relationship between the distribution of training data and the length of trajectory sequences, providing insights into the preprocessing of training data.