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title
COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video Editing
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COVE is a zero-shot and lightweight framework for highly consistent text-guided video editing, supporting videos of any length utilizing text-to-image pretrained diffusion models.
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2026-02-18 05:06:44
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COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video Editing COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video Editing Jiangshan Wang 1* Yue Ma 1* Jiayi Guo 1* Yicheng Xiao 1 Gao Huang 1✝ Xiu Li 1✝ 1 Tsinghua University NeurIPS 2024 * Contribute Equally; ✝ Corresponding Author Paper Code (Coming soon) arXiv Abstract Video editing is an emerging task, in which most current methods adopt the pre-trained text-to-image (T2I) diffusion model to edit the source video in a zero-shot manner. Despite extensive efforts, maintaining the temporal consistency of edited videos remains challenging due to the lack of temporal constraints in the regular T2I diffusion model. To address this issue, we propose COrrespondence-guided Video Editing (COVE), leveraging the inherent diffusion feature correspondence to achieve high-quality and consistent video editing. Specifically, we propose an efficient slidin...
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