EditCtrl：实现实时生成式视频编辑的局部与全局解耦控制

摘要

高保真度生成式视频编辑通过利用预训练视频基础模型，已实现显著的画质提升。然而，这些模型的计算成本构成主要瓶颈——无论修复遮罩的尺寸大小，它们通常都低效地处理完整视频上下文，即使面对稀疏的局部编辑任务也不例外。本文提出EditCtrl，一种高效视频修复控制框架，其计算资源仅聚焦于需要编辑的区域。我们的方法采用创新的局部视频上下文模块，该模块仅对遮罩标记进行操作，使计算成本与编辑尺寸成正比。这种以局部为首要的生成过程随后由轻量级时序全局上下文嵌入器进行引导，该嵌入器以最小开销确保视频全域的上下文一致性。EditCtrl不仅比当前最先进的生成式编辑方法提升10倍计算效率，其编辑质量甚至优于采用全局注意力机制的方法。最后，我们展示了EditCtrl如何解锁多项新功能，包括基于文本提示的多区域编辑与自回归内容传播。

English

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.

EditCtrl：实现实时生成式视频编辑的局部与全局解耦控制

EditCtrl: Disentangled Local and Global Control for Real-Time Generative Video Editing

摘要

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