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

• URL: http://arxiv.org/abs/2312.12635v4
• Date: Fri, 31 Jan 2025 15:34:24 GMT
• Title: RealCraft: Attention Control as A Tool for Zero-Shot Consistent Video Editing
• Authors: Shutong Jin, Ruiyu Wang, Florian T. Pokorny,
• Abstract summary: We propose RealCraft, an attention-control-based method for zero-shot real-world video editing.<n>By swapping cross-attention for new feature injection and relaxing spatial-temporal attention of the editing object, we achieve localized shape-wise edit.<n>We showcase the proposed zero-shot attention-control-based method across a range of videos, demonstrating shape-wise, time-consistent and parameter-free editing.
• Score: 9.215070588761282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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