随时随地：面向具备时空生成动力学的人群仿真编排
Whenever, Wherever: Towards Orchestrating Crowd Simulations with Spatio-Temporal Spawn Dynamics
AI总结：
针对现有数据驱动人群仿真忽略宏观时空生成动力学的问题，提出结合nTPP与生成条件GMM的nTPP-GMM方法，可编排更真实且符合真实场景的人群仿真。
摘要：
真实的人群仿真对于沉浸式虚拟环境至关重要，它依赖于个体行为（微观动力学）和整体人群模式（宏观特征）两方面。尽管近期深度强化学习等数据驱动方法提升了微观层面的真实感，但它们往往忽略了人群密度、流量等关键宏观特征，这些特征由时空生成动力学（即智能体进入场景的时间和位置）决定。传统方法如随机生成速率、随机过程或固定调度，无法保证捕捉潜在的复杂性，或是缺乏多样性与真实感。为解决这一问题，我们提出一种名为nTPP-GMM的新方法，该方法使用神经时间点过程（nTPPs）对时空生成动力学建模，并结合生成条件高斯混合模型（GMM）来确定智能体的生成位置和目标位置。我们通过用nTPP-GMM编排三个不同真实世界数据集的人群仿真来评估该方法。实验表明，采用nTPP-GMM的编排可生成反映真实人群场景的真实仿真结果，并支持人群分析。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
