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arXiv · 2312.12635

RealCraft: Attention Control as A Tool for Zero-Shot Consistent Video Editing

Shutong Jin·Ruiyu Wang·Florian T. Pokorny
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Abstract

Even though large-scale text-to-image generative models show promising performance in synthesizing high-quality images, applying these models directly to image editing remains a significant challenge. This challenge is further amplified in video editing due to the additional dimension of time. This is especially the case for editing real-world videos as it necessitates maintaining a stable structural layout across frames while executing localized edits without disrupting the existing content. In this paper, we propose RealCraft, an attention-control-based method for zero-shot real-world video editing. By swapping cross-attention for new feature injection and relaxing spatial-temporal attention of the editing object, we achieve localized shape-wise edit along with enhanced temporal consistency. Our model directly uses Stable Diffusion and operates without the need for additional information. We showcase the proposed zero-shot attention-control-based method across a range of videos, demonstrating shape-wise, time-consistent and parameter-free editing in videos of up to 64 frames.

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Shutong Jin, Ruiyu Wang, Florian T. Pokorny. 2025-01-31. RealCraft: Attention Control as A Tool for Zero-Shot Consistent Video Editing. https://arxiv.org/abs/2312.12635

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