用时空生成模型让人群出场更真实，提升虚拟场景沉浸感。
Whenever, Wherever: Towards Orchestrating Crowd Simulations with Spatio-Temporal Spawn Dynamics
- 用神经时间点过程+高斯混合模型建模人群出场时间和位置
- 在3个真实数据集上实现符合实际密度与流动性的仿真
- 适合做虚拟现实、交通规划的开发者参考
真实人群仿真对沉浸式虚拟环境至关重要，既需个体行为（微观动态），也需整体分布特征（宏观特性）。当前基于深度强化学习的方法虽提升微观真实性，却常忽略人群密度与流动等宏观特征，而这些特征由时空生成动态决定，即代理何时何地进入场景。传统方法如随机生成率、随机过程或固定调度，难以捕捉复杂性且缺乏多样性与真实性。为此，我们提出nTPP-GMM方法，利用神经时间点过程（nTPPs）结合生成条件高斯混合模型（GMM）来建模代理的生成与目标位置。我们在三个不同真实世界数据集上评估该方法，实验表明，使用nTPP-GMM进行人群编排可生成反映真实场景的仿真结果，并支持人群分析。
原文摘要 · Abstract (English)
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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