用AI自动生成逼真、安全的无人机灯光秀动画
Generative AI for Safe and Photorealistic Drone Light Shows
- 输入文字提示，直接生成逼真的无人机编队动画
- 支持2000架模拟无人机和49架真实无人机同步运行
- 全程在普通电脑上运行，适合创意设计与演出策划
无人机灯光秀正在重塑空中娱乐形式，但其大规模应用受限于人工手动制作动画的高成本。现有生成式AI框架难以实现逼真的动态运动与视觉效果。为此，我们提出SWAN——一个端到端流程，可直接根据文本提示生成逼真、大规模且无碰撞的无人机编队动画。SWAN将文本转化为参考视频，并通过新型自适应点追踪算法，将像素空间动态映射为物理集群运动轨迹，有效应对严重遮挡与拓扑剧烈变化。随后的规划器分配轨迹至各无人机，安全过滤器确保无碰撞执行。我们在仿真中成功协调2000架无人机，在真实场景中验证了49架四旋翼无人机的可行性，所有操作均在标准消费级硬件上完成。该工作展示了生成式AI如何实现多机器人编排自动化，为无人机灯光秀提供可访问的新范式。
原文摘要 · Abstract (English)
Drone light shows are redefining aerial entertainment, yet their widespread adoption is bottlenecked by labor-intensive, manual animation. While generative AI promises an automated alternative, current frameworks fail to provide photorealism with fluid, dynamic motion. To address this limitation, we introduce SWAN, an end-to-end pipeline that synthesizes photorealistic, large-scale, and collision-free drone choreographies directly from text prompts. SWAN converts text into realistic reference videos and translates these pixel-space dynamics into physical swarm kinematics using a novel, adaptive point-tracking algorithm. Unlike existing trackers, this method maintains spatial coherence through severe occlusions and rapid topological shifts. A dedicated planner then allocates these trajectories to individual drones, while a subsequent safety filter ensures collision-free execution. We demonstrate scalability by safely orchestrating simulated 2,000-drone formations and validate physical feasibility on a dense real-world swarm of 49 quadcopters, operating everything entirely on standard consumer hardware. Combined, this work demonstrates how generative AI can be leveraged to automate multi-robot choreography design, providing an accessible new framework for drone light shows.
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