Whenever, Wherever: Towards Orchestrating Crowd Simulations with Spatio-Temporal Spawn Dynamics

• URL: http://arxiv.org/abs/2503.16639v1
• Date: Thu, 20 Mar 2025 18:46:41 GMT
• Title: Whenever, Wherever: Towards Orchestrating Crowd Simulations with Spatio-Temporal Spawn Dynamics
• Authors: Thomas Kreutz, Max Mühlhäuser, Alejandro Sanchez Guinea,
• Abstract summary: We propose nTPP-GMM that models spawn-temporal spawn dynamics using Neural Temporal Point Processes.<n>We evaluate our approach by simulations of three diverse real-world datasets with nTPP-GMM.
• Score: 65.72663487116439
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Realistic crowd simulations are essential for immersive virtual environments, relying on both individual behaviors (microscopic dynamics) and overall crowd patterns (macroscopic characteristics). While recent data-driven methods like deep reinforcement learning improve microscopic realism, they often overlook critical macroscopic features such as crowd density and flow, which are governed by spatio-temporal spawn dynamics, namely, when and where agents enter a scene. Traditional methods, like random spawn rates, stochastic processes, or fixed schedules, are not guaranteed to capture the underlying complexity or lack diversity and realism. To address this issue, we propose a novel approach called nTPP-GMM that models spatio-temporal spawn dynamics using Neural Temporal Point Processes (nTPPs) that are coupled with a spawn-conditional Gaussian Mixture Model (GMM) for agent spawn and goal positions. We evaluate our approach by orchestrating crowd simulations of three diverse real-world datasets with nTPP-GMM. Our experiments demonstrate the orchestration with nTPP-GMM leads to realistic simulations that reflect real-world crowd scenarios and allow crowd analysis.

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