EditCtrl: 解耦的局部和全局控制用于实时生成视频编辑
EditCtrl: Disentangled Local and Global Control for Real-Time Generative Video Editing
AI总结：
本文提出EditCtrl，通过局部和全局控制模块实现高效视频修复，提升编辑质量和计算效率，支持多区域编辑和内容传播。
摘要：
高保真生成视频编辑通过利用预训练视频基础模型显著提升了质量，但其计算成本是主要瓶颈，因为它们通常设计为无效处理完整视频上下文，无论修补掩码的大小，即使对于稀疏、局部化编辑。在本文中，我们引入EditCtrl，一种高效的视频修补控制框架，仅在需要的地方进行计算。我们的方法包含一个新颖的局部视频上下文模块，仅在遮蔽标记上操作，产生与编辑大小成比例的计算成本。此局部优先生成随后由轻量级时间全局上下文嵌入器引导，确保视频范围内的上下文一致性，具有最小的开销。不仅EditCtrl比最先进的生成编辑方法计算效率高10倍，而且相比全注意力设计的方法，其编辑质量也有所提高。最后，我们展示了EditCtrl如何解锁新的能力，包括使用文本提示的多区域编辑和自回归内容传播。
英文摘要：
High-fidelity generative video editing has seen significant quality improvements by leveraging pre-trained video foundation models. However, their computational cost is a major bottleneck, as they are often designed to inefficiently process the full video context regardless of the inpainting mask's size, even for sparse, localized edits. In this paper, we introduce EditCtrl, an efficient video inpainting control framework that focuses computation only where it is needed. Our approach features a novel local video context module that operates solely on masked tokens, yielding a computational cost proportional to the edit size. This local-first generation is then guided by a lightweight temporal global context embedder that ensures video-wide context consistency with minimal overhead. Not only is EditCtrl 10 times more compute efficient than state-of-the-art generative editing methods, it even improves editing quality compared to methods designed with full-attention. Finally, we showcase how EditCtrl unlocks new capabilities, including multi-region editing with text prompts and autoregressive content propagation.
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。
