Consistent and Editable: A Balanced Framework for Text-Guided Video Editing

2026-07-06

AI Summary

Existing text-guided video editing methods often struggle to simultaneously achieve temporal consistency and editability, typically requiring a trade-off between the two. This work proposes EquiEdit, a novel framework that jointly optimizes both aspects to enable high-quality video editing. The approach introduces a temporal Mamba module to enhance inter-frame consistency and incorporates a spectral-transform-based noise injection strategy that preserves the structure of the initial latent-space noise while improving editing flexibility. Built upon a diffusion model architecture, EquiEdit further integrates a temporal-aware scanning mechanism to better capture dynamic content across frames. Experimental results demonstrate that EquiEdit significantly outperforms existing methods in terms of temporal consistency, editability, and fidelity to the input video.

Abstract

Recently, diffusion models have achieved considerable success in the text-guided video editing domain. However, existing works often struggle to balance the trade-off between temporal consistency and editability in video editing, with consistency and editability typically being inversely related. To address this, we propose a high-quality video editing framework enhanced for consistency and editability, named EquiEdit, which improves coordinatively the temporal consistency and editability of the edited videos while achieving a balance between the two. In terms of temporal consistency, the proposed temporal Mamba module with a tailored temporal-aware scanning scans fused video sequences following four designed directions, effectively enhancing the inter-frame consistency of edited videos. For editability, we design a noise injection strategy based on the spectral transformation to increase editing flexibility, where the Fourier transform is used to preserve the hidden structure in the initial latent noise used for editing, ensuring inter-frame consistency of the edited video and fidelity to the input video. Extensive qualitative and quantitative experiments demonstrate the effectiveness of our method in terms of temporal consistency and editability, as well as its great fidelity to the input video itself.

Problem

• text-guided video editing
• temporal consistency
• editability
• diffusion models

Innovation

• temporal consistency
• editability
• temporal Mamba
• spectral transformation
• diffusion models