只计算需要修改的部分，实现高效高质量视频编辑。
EditCtrl: Disentangled Local and Global Control for Real-Time Generative Video Editing
- 仅在需要编辑区域计算，降低算力消耗
- 比顶尖方法快10倍，且画质更优
- 支持多区域编辑和文本控制，适合实时应用
高质量生成式视频编辑借助预训练视频基础模型取得显著进展，但其计算开销巨大，常因处理整个视频上下文而效率低下，即使仅需局部修改。本文提出EditCtrl，一种高效的视频修复控制框架，仅在编辑区域进行计算。其创新的局部视频上下文模块仅处理被遮挡区域的标记，计算成本与编辑范围成正比。随后通过轻量级时序全局上下文嵌入器，以极小开销确保全视频一致性。EditCtrl相比当前最优方法提升10倍计算效率，且编辑质量更优。此外，该框架还实现了多区域编辑、文本提示控制及自回归内容传播等新能力。
原文摘要 · Abstract (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.
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