用扩散模型模拟人群，兼顾环境与个体-群体互动
EnvSocial-Diff: A Diffusion-Based Crowd Simulation Model with Environmental Conditioning and Individual-Group Interaction
- 基于扩散模型，结合社会物理与环境条件建模
- 在多个基准数据集上超越现有最先进方法
- 适合需要高真实感人群模拟的研究者
真实行人轨迹建模需同时考虑社会互动与环境背景，但现有方法多侧重社会动力学。我们提出EnvSocial-Diff：一种基于扩散的群体现象模拟模型，融合社会物理原理，增强环境条件约束与个体-群体交互。其结构化环境编码模块显式建模障碍物、兴趣点与光照水平，提供可解释的场景约束与吸引信号。并行设计的个体-群体交互模块通过图结构捕捉微观人际关系与宏观群体一致性。在多个基准数据集上的实验表明，该模型优于最新最先进方法，验证了显式环境条件与多层次社会交互对真实感群体现象模拟的重要性。代码已开源：https://github.com/zqyq/EnvSocial-Diff。
原文摘要 · Abstract (English)
Modeling realistic pedestrian trajectories requires accounting for both social interactions and environmental context, yet most existing approaches largely emphasize social dynamics. We propose \textbf{EnvSocial-Diff}: a diffusion-based crowd simulation model informed by social physics and augmented with environmental conditioning and individual--group interaction. Our structured environmental conditioning module explicitly encodes obstacles, objects of interest, and lighting levels, providing interpretable signals that capture scene constraints and attractors. In parallel, the individual--group interaction module goes beyond individual-level modeling by capturing both fine-grained interpersonal relations and group-level conformity through a graph-based design. Experiments on multiple benchmark datasets demonstrate that EnvSocial-Diff outperforms the latest state-of-the-art methods, underscoring the importance of explicit environmental conditioning and multi-level social interaction for realistic crowd simulation. Code is here: https://github.com/zqyq/EnvSocial-Diff.
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