外观
论文arXiv 2610.0943821 天前
通过 World-Model 规划实现可控人群生成
原文链接：Controllable Crowd Generation through World-Model Planning（arXiv 2610.09438 · 发布日期：2026-10-07）
Controllable Crowd Generation through World-Model Planning
Ctrl-CWM用想象未来的World-Model规划整合人群生成与运行时控制，并在行人视频学习及避让、吸引场景中验证。
核心要点
- 现有依赖预设控制的方法难以灵活适应新的用户指定目标。
- Ctrl-CWM包含编码器、actor、critic和规划器，以想象轨迹评估并选择动作。
- 方法先在真实行人视频上学习人体运动动态，并冻结编码器以保留表示。
- 在不同到达条件和避让、吸引控制中，多数真实感与碰撞指标优于最先进方法，并提供项目页。
验证范围与局限：验证范围为人群仿真中的不同到达条件，以及避让和吸引运行时控制场景。
英文摘要 (Abstract)
Crowd simulation plays a central role in robot navigation, autonomous driving, and urban planning. For these applications, realistic simulation requires crowds to adapt their behavior to environmental changes and user objectives. However, existing methods that rely on predefined control settings have limited flexibility in accommodating new user-specified objectives. To address this limitation, we propose Ctrl-CWM, a multi-agent Controllable Crowd World Model that integrates crowd generation and run-time control. Our key idea is to adapt the world-model principle of planning using imagined futures to crowd simulation. To this end, Ctrl-CWM consists of an encoder that learns a representation of human motion dynamics, an actor that proposes pedestrian displacements, a critic that evaluates imagined crowd trajectories, and a planner that selects actions. We first learn human motion dynamics through trajectory prediction on real-world pedestrian videos and then freeze the encoder to preserve them. Using this representation, the actor generates imagined crowd trajectories through repeated state updates, and the planner combines the critic's scores with user costs to select actions. Repeated planning advances the simulated crowd, while additional user costs introduce new control objectives without retraining. We extensively evaluate crowd generation under varied agent arrival conditions and run-time control across avoidance and attraction scenarios. Ctrl-CWM outperforms the state-of-the-art method on most crowd realism and collision metrics, and adapts crowd behaviors to user-specified objectives introduced during simulation. The project page is available at https://jungyu0413.github.io/Ctrl-CWM
作者：JunGyu Lee, Jisu Shin, Seunghyun Shin, Hae‐Gon Jeon
来源
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