CVJun 7

Beyond Consistency: Preserving Temporal Structure in Zero-Shot Video Editing

Deyin Liu, Yisheng Ding, Zhe Jin, Xiatian Zhu, Anjan Dutta, Lin Wu
arXiv:2606.08780v19.2
Predicted impact top 31% in CV · last 90 daysOriginality Highly original
AI Analysis

For video editing practitioners, this work addresses the critical problem of maintaining narrative coherence in zero-shot editing, which was previously unaddressed.

Existing zero-shot video editing methods fail to preserve the original temporal structure of videos, leading to narratively incoherent outputs. The proposed method achieves state-of-the-art results by preserving temporal structure through adaptive video partitioning and clip-adaptive token merging, setting a new benchmark for editing fidelity.

Existing zero-shot video editing methods rely on pre-trained diffusion models, successfully achieving spatial control and basic temporal consistency but fundamentally fail to preserve the video's original temporal structure.This distinction is critical: temporal consistency ensures visual smoothness, but temporal structure dictates the video's high-level narrative, rhythm, and semantic flow. Without this preservation, the edited output, especially for long videos with complex semantic variations, becomes narratively incoherent and semantically ambiguous. To address this limitation, we introduce a novel zero-shot editing approach that, for the first time, explicitly focuses on preserving the source video's temporal structure. We achieve this by adaptively partitioning the video into semantically distinct clips based on feature similarity and selecting a representative anchor frame for each clip. To enhance both intra-clip fidelity and computational efficiency, we design a clip-adaptive token merging strategy which leverages the anchor's semantic dominance to stabilize the editing. Furthermore, we employ an alternating combination strategy that ensures seamless inter-clip transitions while maintaining semantic distinction. Extensive experiments demonstrate that our method achieves state-of-the-art results, successfully balancing the preservation of original temporal structure with computational efficiency, and setting a new benchmark for zero-shot video editing fidelity.

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