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Multi-Perception Crowd: Learning to Combine Entity and Implicit Perception for Diverse Crowd Simulation
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DOI：10.1109/tvcg.2026.3707675.png)
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Abstract
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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: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">entity perception</i>, which handles hard constraints imposed by physical obstacles, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">implicit environmental perception</i>, 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.
Keywords:
Crowd simulation
reinforcement learning
crowd authoring
animations
Journal
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6.5
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294
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2.2W
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Cited Papers