FREE-Edit: Using Editing-aware Injection in Rectified Flow Models for Zero-shot Image-Driven Video Editing

• URL: http://arxiv.org/abs/2603.01164v1
• Date: Sun, 01 Mar 2026 16:01:44 GMT
• Title: FREE-Edit: Using Editing-aware Injection in Rectified Flow Models for Zero-shot Image-Driven Video Editing
• Authors: Maomao Li, Yunfei Liu, Yu Li,
• Abstract summary: Image-driven video editing aims to propagate edit contents from the modified first frame to the rest frames.<n>Current methods usually invert the source video to noise using a pre-trained image-to-video (I2V) model and then guide the sampling process using the edited first frame.<n>We propose an Editing-awaRE (REE) injection method to modulate injection intensity of each token.
• Score: 12.549184989151135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image-driven video editing aims to propagate edit contents from the modified first frame to the rest frames. The existing methods usually invert the source video to noise using a pre-trained image-to-video (I2V) model and then guide the sampling process using the edited first frame. Generally, a popular choice for maintaining motion and layout from the source video is intervening in the denoising process by injecting attention during reconstruction. However, such injection often leads to unsatisfactory results, where excessive injection leads to conflicting semantics from the source video while insufficient injection brings limited source representation. Recognizing this, we propose an Editing-awaRE (REE) injection method to modulate injection intensity of each token. Specifically, we first compute the pixel difference between the source and edited first frame to form a corresponding editing mask. Next, we track the editing area throughout the entire video by using optical flow to warp the first-frame mask. Then, editing-aware feature injection intensity for each token is generated accordingly, where injection is not conducted on editing areas. Building upon REE injection, we further propose a zero-shot image-driven video editing framework with recent-emerging rectified-Flow models, dubbed FREE-Edit. Without fine-tuning or training, our FREE-Edit demonstrates effectiveness in various image-driven video editing scenarios, showing its capability to produce higher-quality outputs compared with existing techniques. Project page: https://free-edit.github.io/page/.

Related papers

• ProEdit: Inversion-based Editing From Prompts Done Right [63.554692704101]
  Inversion-based visual editing provides an effective and training-free way to edit an image or a video based on user instructions.<n>Existing methods typically inject source image information during the sampling process to maintain editing consistency.<n>We propose ProEdit to address this issue both in the attention and the latent aspects.
  arXiv  Detail & Related papers  (2025-12-26T18:59:14Z)
• Taming Flow-based I2V Models for Creative Video Editing [64.67801702413122]
  Video editing, which aims to manipulate videos according to user intent, remains an emerging challenge.<n>Most existing image-conditioned video editing methods require inversion with model-specific design or need extensive optimization.<n>We propose IF-V2V, an Inversion-Free method that can adapt off-the-shelf flow-matching-based I2V models for video editing without significant computational overhead.
  arXiv  Detail & Related papers  (2025-09-26T05:57:04Z)
• 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)
• 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)
• Ground-A-Video: Zero-shot Grounded Video Editing using Text-to-image Diffusion Models [65.268245109828]
  Ground-A-Video is a video-to-video translation framework for multi-attribute video editing. It attains temporally consistent editing of input videos in a training-free manner. Experiments and applications demonstrate that Ground-A-Video's zero-shot capacity outperforms other baseline methods in terms of edit-accuracy and frame consistency.
  arXiv  Detail & Related papers  (2023-10-02T11:28:37Z)
• InFusion: Inject and Attention Fusion for Multi Concept Zero-Shot Text-based Video Editing [27.661609140918916]
  InFusion is a framework for zero-shot text-based video editing. It supports editing of multiple concepts with pixel-level control over diverse concepts mentioned in the editing prompt. Our framework is a low-cost alternative to one-shot tuned models for editing since it does not require training.
  arXiv  Detail & Related papers  (2023-07-22T17:05:47Z)
• Pix2Video: Video Editing using Image Diffusion [43.07444438561277]
  We investigate how to use pre-trained image models for text-guided video editing. Our method works in two simple steps: first, we use a pre-trained structure-guided (e.g., depth) image diffusion model to perform text-guided edits on an anchor frame. We demonstrate that realistic text-guided video edits are possible, without any compute-intensive preprocessing or video-specific finetuning.
  arXiv  Detail & Related papers  (2023-03-22T16:36:10Z)
• FateZero: Fusing Attentions for Zero-shot Text-based Video Editing [104.27329655124299]
  We propose FateZero, a zero-shot text-based editing method on real-world videos without per-prompt training or use-specific mask. Our method is the first one to show the ability of zero-shot text-driven video style and local attribute editing from the trained text-to-image model.
  arXiv  Detail & Related papers  (2023-03-16T17:51:13Z)
• 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.