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

• URL: http://arxiv.org/abs/2607.05056v1
• Date: Mon, 06 Jul 2026 13:29:28 GMT
• Title: Consistent and Editable: A Balanced Framework for Text-Guided Video Editing
• Abstract summary: We propose a high-quality video editing framework enhanced for consistency and editability, named EquiEdit.<n>In terms of temporal consistency, the proposed temporal Mamba module with a tailored temporal-aware scanning scans fused video sequences following four designed directions.<n>For editability, we design a noise injection strategy based on the spectral transformation to increase editing flexibility, ensuring inter-frame consistency of the edited video and fidelity to the input video.
• Score: 11.907018651555475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• In-Context Sync-LoRA for Portrait Video Editing [66.21215915461069]
  Sync-LoRA is a method for editing portrait videos that achieves high-quality visual modifications.<n>We train an in-context LoRA using paired videos that depict identical motion trajectories but differ in appearance.<n>This training setup teaches the model to combine motion cues from the source video with the visual changes introduced in the edited first frame.
  arXiv  Detail & Related papers  (2025-12-02T18:40:35Z)
• Re-Attentional Controllable Video Diffusion Editing [48.052781838711994]
  We propose a Re-Attentional Controllable Video Diffusion Editing (ReAtCo) method.<n>To align the spatial placement of the target objects with the edited text prompt in a training-free manner, we propose a Re-Attentional Diffusion (RAD)<n>RAD refocuses the cross-attention activation responses between the edited text prompt and the target video during the denoising stage, resulting in a spatially location-aligned and semantically high-fidelity manipulated video.
  arXiv  Detail & Related papers  (2024-12-16T12:32:21Z)
• StableV2V: Stablizing Shape Consistency in Video-to-Video Editing [11.09708780767668]
  We present a shape-consistent video editing method, namely StableV2V, in this paper. Our method decomposes the entire editing pipeline into several sequential procedures, where it edits the first video frame, then establishes an alignment between the delivered motions and user prompts, and eventually propagates the edited contents to all other frames based on such alignment. Experimental results and analyses illustrate the outperforming performance, visual consistency, and inference efficiency of our method compared to existing state-of-the-art studies.
  arXiv  Detail & Related papers  (2024-11-17T11:48:01Z)
• VIA: Unified Spatiotemporal Video Adaptation Framework for Global and Local Video Editing [91.60658973688996]
  We introduce VIA, a unified Video Adaptation framework for global local video editing, pushing the limits of consistently editing minute-long videos.<n>To ensure local consistency within individual frames, we designed test-time editing adaptation to adapt a pre-trained image editing model.<n>We show that VIA can achieve consistent long video editing in minutes, unlocking the potential for advanced video editing tasks over long video sequences.
  arXiv  Detail & Related papers  (2024-06-18T17:51:37Z)
• I2VEdit: First-Frame-Guided Video Editing via Image-to-Video Diffusion Models [18.36472998650704]
  We introduce a novel and generic solution that extends the applicability of image editing tools to videos by propagating edits from a single frame to the entire video using a pre-trained image-to-video model. Our method, dubbed I2VEdit, adaptively preserves the visual and motion integrity of the source video depending on the extent of the edits.
  arXiv  Detail & Related papers  (2024-05-26T11:47:40Z)
• FastVideoEdit: Leveraging Consistency Models for Efficient Text-to-Video Editing [8.907836546058086]
  Existing approaches relying on image generation models for video editing suffer from time-consuming one-shot fine-tuning, additional condition extraction, or DDIM inversion. We propose FastVideoEdit, an efficient zero-shot video editing approach inspired by Consistency Models (CMs) Our method enables direct mapping from source video to target video with strong preservation ability utilizing a special variance schedule.
  arXiv  Detail & Related papers  (2024-03-10T17:12:01Z)
• MotionEditor: Editing Video Motion via Content-Aware Diffusion [96.825431998349]
  MotionEditor is a diffusion model for video motion editing. It incorporates a novel content-aware motion adapter into ControlNet to capture temporal motion correspondence.
  arXiv  Detail & Related papers  (2023-11-30T18:59:33Z)
• Edit Temporal-Consistent Videos with Image Diffusion Model [49.88186997567138]
  Large-scale text-to-image (T2I) diffusion models have been extended for text-guided video editing. T achieves state-of-the-art performance in both video temporal consistency and video editing capability.
  arXiv  Detail & Related papers  (2023-08-17T16:40:55Z)
• Edit-A-Video: Single Video Editing with Object-Aware Consistency [49.43316939996227]
  We propose a video editing framework given only a pretrained TTI model and a single text, video> pair, which we term Edit-A-Video. The framework consists of two stages: (1) inflating the 2D model into the 3D model by appending temporal modules tuning and on the source video (2) inverting the source video into the noise and editing with target text prompt and attention map injection. We present extensive experimental results over various types of text and videos, and demonstrate the superiority of the proposed method compared to baselines in terms of background consistency, text alignment, and video editing quality.
  arXiv  Detail & Related papers  (2023-03-14T14:35:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.