Continuous Locomotive Crowd Behavior Generation

• URL: http://arxiv.org/abs/2504.04756v2
• Date: Mon, 21 Apr 2025 11:42:20 GMT
• Title: Continuous Locomotive Crowd Behavior Generation
• Authors: Inhwan Bae, Junoh Lee, Hae-Gon Jeon,
• Abstract summary: We introduce a novel method for automatically generating continuous, realistic crowd trajectories with heterogeneous behaviors and interactions.<n>We demonstrate that our approach effectively models diverse crowd behavior patterns and generalizes well across different geographical environments.
• Score: 23.45902601618188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling and reproducing crowd behaviors are important in various domains including psychology, robotics, transport engineering and virtual environments. Conventional methods have focused on synthesizing momentary scenes, which have difficulty in replicating the continuous nature of real-world crowds. In this paper, we introduce a novel method for automatically generating continuous, realistic crowd trajectories with heterogeneous behaviors and interactions among individuals. We first design a crowd emitter model. To do this, we obtain spatial layouts from single input images, including a segmentation map, appearance map, population density map and population probability, prior to crowd generation. The emitter then continually places individuals on the timeline by assigning independent behavior characteristics such as agents' type, pace, and start/end positions using diffusion models. Next, our crowd simulator produces their long-term locomotions. To simulate diverse actions, it can augment their behaviors based on a Markov chain. As a result, our overall framework populates the scenes with heterogeneous crowd behaviors by alternating between the proposed emitter and simulator. Note that all the components in the proposed framework are user-controllable. Lastly, we propose a benchmark protocol to evaluate the realism and quality of the generated crowds in terms of the scene-level population dynamics and the individual-level trajectory accuracy. We demonstrate that our approach effectively models diverse crowd behavior patterns and generalizes well across different geographical environments. Code is publicly available at https://github.com/InhwanBae/CrowdES .

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