EditCtrl: Disentangled Local and Global Control for Real-Time Generative Video Editing
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.
699494bad2ea89ac106cf9e7
Yehonathan Litman
699494bad2ea89ac106cf9e8
Shikun Liu
699494bad2ea89ac106cf9e9
Dario Seyb
699494bad2ea89ac106cf9ea
Nicholas Milef
699494bad2ea89ac106cf9eb
Yang Zhou
699494bad2ea89ac106cf9ec
Carl Marshall
699494bad2ea89ac106cf9ed
Shubham Tulsiani
699494bad2ea89ac106cf9ee
Caleb Leak
EditCtrl: Disentangled Local and Global Control for Real-Time Generative Video Editing
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.
Efficient video inpainting framework that focuses computation on masked regions while maintaining global context consistency through a lightweight embedder.
video foundation models video inpainting computational efficiency masked tokens local video context module temporal global context embedder autoregressive content propagation