Gen-Swarms is an innovative method that leverages and combines the capabilities of deep generative models with reactive navigation algorithms to automate the creation of drone shows. Advancements in deep generative models, particularly diffusion models, have demonstrated remarkable effectiveness in generating high-quality 2D images. Building on this success, various works have extended diffusion models to 3D point cloud generation. In contrast, alternative generative models such as flow matching have been proposed, offering a simple and intuitive transition from noise to meaningful outputs. However, the application of flow matching models to 3D point cloud generation remains largely unexplored. Gen-Swarms adapts these models to automatically generate drone shows. Existing 3D point cloud generative models create point trajectories which are impractical for drone swarms. In contrast, our method not only generates accurate 3D shapes but also guides the swarm motion, producing smooth trajectories and accounting for potential collisions through a reactive navigation algorithm incorporated into the sampling process. For example, when given a text category like Airplane, Gen-Swarms can rapidly and continuously generate numerous variations of 3D airplane shapes. Our experiments demonstrate that this approach is particularly well-suited for drone shows, providing feasible trajectories, creating representative final shapes, and significantly enhancing the overall performance of drone show generation.

翻译：Gen-Swarms 是一种创新方法，它利用并结合深度生成模型与反应式导航算法的能力，以实现无人机表演的自动化创建。深度生成模型（尤其是扩散模型）的进展已展现出在生成高质量二维图像方面的显著成效。基于这一成功，多项研究已将扩散模型扩展至三维点云生成领域。相比之下，其他生成模型（如流匹配模型）也被提出，其提供了从噪声到有意义输出的简单直观转换路径。然而，流匹配模型在三维点云生成中的应用仍很大程度上未被探索。Gen-Swarms 对这些模型进行适配，以自动生成无人机表演。现有的三维点云生成模型所创建的点轨迹对于无人机集群而言并不实用。与此不同，我们的方法不仅生成精确的三维形状，还通过整合到采样过程中的反应式导航算法来引导集群运动，生成平滑轨迹并考虑潜在的碰撞风险。例如，当给定如“飞机”这样的文本类别时，Gen-Swarms 能够快速且连续地生成大量三维飞机形状的变体。我们的实验表明，该方法特别适用于无人机表演，能提供可行的轨迹、生成具有代表性的最终形状，并显著提升无人机表演生成的整体性能。