Gen-Swarms：将深度生成模型适配到无人机集群
Gen-Swarms: Adapting Deep Generative Models to Swarms of Drones
AI总结：
Gen-Swarms将深度生成模型与反应式导航算法结合，用于自动生成无人机表演，生成精确3D形状和可行轨迹，显著提升表演生成性能。
摘要：
Gen-Swarms是一种创新方法，它利用并整合了深度生成模型与反应式导航算法的能力，以自动化创建无人机表演。深度生成模型的进展，特别是扩散模型，在生成高质量2D图像方面已展现出显著效果。基于这一成功，多项工作已将扩散模型扩展到3D点云生成。相比之下，诸如流匹配等替代生成模型已被提出，提供了从噪声到有意义输出的简单直观的过渡。然而，流匹配模型在3D点云生成中的应用在很大程度上仍未被探索。Gen-Swarms适配这些模型以自动生成无人机表演。现有的3D点云生成模型生成的点轨迹对无人机集群而言不切实际。相比之下，我们的方法不仅生成精确的3D形状，还通过将反应式导航算法纳入采样过程来引导集群运动，产生平滑轨迹并考虑潜在碰撞。例如，当给定如“飞机”这样的文本类别时，Gen-Swarms可以快速且连续地生成大量3D飞机形状的变体。我们的实验表明，这种方法特别适合无人机表演，提供可行的轨迹，创建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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
