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Transforming Image Editors into Video Editors

Feng Wang, Zijie Li, Ceyuan Yang, Alan Yuille, Peng Wang

arXiv:2610.11037v1cs.CV

TL;DR

Video editing remains more difficult and costly than image editing. AVE edits sparse keyframes with an image editor, then uses motion-guided video diffusion to propagate edits; it reports strong results across IVEBench and VIE-Bench.

  • Problem

    Video editing lags image editing in visual quality, semantic consistency, and instruction following, while longer contexts and harder data collection make it more costly.

    Source: §1 · p. 1

  • Method

    AVE edits selected keyframes with a powerful image editor, then generates the video with motion-guided image-to-video diffusion using the edited frames as fixed anchors.

    Source: §1 · p. 2

  • Results

    AVE reports strong, balanced performance across IVEBench and VIE-Bench; on IVEBench's short subset, total score rises from 66.75 to 70.58.

    Source: §1 · p. 2, §4.2 · p. 6

  • Takeaways & Limitations

    Ablations find final video editing quality strongly correlated with the quality of the Stage-1 image editor.

    Source: §4.3 · p. 8

Abstract

from arXiv · show

Recent image editing systems have achieved impressive semantic understanding, visual fidelity, and instruction-following ability, while video editing remains substantially more difficult and costly. In this paper, we present a simple alternative to end-to-end video editing: instead of training a monolithic video editor, we transform a strong image editor into a video editor through anchor-based generation. Our key insight is that video editing can be decomposed into two subproblems: editing a sparse set of keyframes and propagating those edits across time. Based on this observation, we propose Anchor-based Video Editing (AVE), a two-stage framework in which a powerful image editor first performs composed editing on selected keyframes, and a motion-guided image-to-video diffusion model then generates the final video by treating the edited keyframes as fixed anchors. This design directly inherits the strengths of modern image editors while avoiding expensive end-to-end video editing training. Experiments on IVEBench and VIE-Bench show that AVE achieves strong performance in instruction following, temporal consistency, and content fidelity. Further ablations reveal that final video editing quality is strongly correlated with the quality of the image editor, suggesting that future progress in video editing may come from stronger image editing foundations and lightweight transfer to video. Code is available at https://github.com/wangf3014/AVE.

1 Introduction

Image editing has advanced while video editing remains behind and costly to train. AVE addresses this gap by editing selected keyframes with an image editor and propagating those edits through video generation.

1 Introduction

  • AVE frames video editing as two subproblems: semantic editing on sparse keyframes and temporally coherent interpolation between them.The paper argues that the main difficulty lies in image-level semantic editing, while temporal information can be handled with relatively little additional training.Source: §1 · pp. 1–2
  • Table 1 compares three video editing paradigms and characterizes AVE as favorable in consistency, frame-wise quality, and instruction following at normal cost.The paper presents this as an intuitive comparison of the paradigms.Source: Table 1 · p. 2, §1 · p. 2
  • AVE jointly edits selected keyframes with a powerful image editor, then uses motion-guided image-to-video diffusion to propagate edits across intermediate frames.The edited keyframes serve as fixed anchors, while source-video motion guides generation.Source: §1 · p. 2
  • Compared with end-to-end and per-frame editing, AVE decouples its stages, uses a lightweight motion encoder, and interpolates between keyframes to support temporal consistency.The paper reports that a small motion encoder and relatively short context length suffice for its motion information needs.Source: §1 · p. 2
  • AVE achieves strong, balanced results across IVEBench and VIE-Bench, including instruction following, temporal consistency, content fidelity, and diverse editing types.The IVEBench results are especially strong on motion-related metrics; ablations also find robustness to keyframe selection strategies.Source: §1 · p. 2

2 Related Work

Related work spans advances in image editing, instruction-guided video editing, and image-to-video generation. AVE draws on these lines by delegating semantic edits to image editors and using video models for motion-guided interpolation.

Instruction-based image editing

  • Instruction-based image editors have progressed from diffusion systems such as InstructPix2Pix [Brooks et al., 2023] to multimodal systems such as BAGEL [Deng et al., 2025].Recent open and commercial systems include Qwen-Image-Edit, FLUX.1 Kontext, and Nano Banana 2.Source: §2 · pp. 2–3

Instruction-guided video editing

  • Instruction-guided video editing methods range from inversion and feature propagation to feed-forward, end-to-end, unified, and in-context approaches.The paper notes that these methods typically require substantial video-specific data and computation, and their quality still trails the best image editors.Source: §2 · p. 3

Image-to-video generation and motion modeling

  • Image-to-video work shows that motion can be modeled separately from appearance and that pretrained image generators can be extended with lightweight temporal modules.AVE applies this idea to editing by interpolating between edited keyframes under source-video motion cues.Source: §2 · p. 3

3 Method

AVE separates video editing into semantic editing of sparse keyframes and temporally coherent interpolation. Edited keyframes connect the stages as fixed anchors, alongside a lightweight representation of source motion.

3.1 Overview

  • AVE edits selected keyframes with an image editor, then generates video from those anchors and lightweight source-motion features; edited keyframes are the stages’ only interface.This decoupling lets the image editor be replaced without retraining the video generator.Source: §3.1 · p. 3

3.2 Stage-1: Composed Image Editing

  • For open-source use, BAGEL is fine-tuned on aligned keyframes sampled from private video-editing triplets; about 1.5M examples support jointly consistent edits.The framework also supports API-based editors; editing selected keyframes in one multi-turn conversation can preserve identity, style, and scene-level consistency.Source: §3.2 · pp. 3–4

3.3 Stage-2: Keyframe Interpolation

  • A six-layer factorized Transformer applies spatial then temporal attention to video latents, producing motion features that condition the frozen image-to-video backbone.The motion representation enters Wan2.2 through a ControlNet-style residual branch, leaving appearance and editing semantics to the anchors.Source: §3.3 · p. 4, Figure 2 · p. 5
  • Edited keyframe latents are clamped throughout reverse diffusion, while non-anchor frames are initialized with noise and generated between the fixed anchors.The resulting video is decoded to pixel space after denoising.Source: §3.3 · p. 5

4 Experiments

AVE performs strongly across IVEBench and VIE-Bench, while ablations connect final editing quality to the Stage-1 image editor and show robustness to keyframe selection. Its results include gains across multiple editing dimensions and diverse qualitative examples.

4.2 Main Results

  • 70.58 (up from the previous best 66.75) is AVE’s IVEBench short-subset total score; it leads most reported metrics on both subsets and scores 68.75 on the long-subset total.On the short subset, AVE also records video quality 82.10, instruction compliance 49.65, and semantic consistency 26.12.Source: Table 2 · p. 6, Table 3 · p. 6, §4.2 · p. 6
  • On VIE-Bench, AVE leads swap/change (9.448), remove (9.352), averaged instruction following (9.539), and preservation (9.425).The benchmark covers add, swap, remove, and style/tone editing categories.Source: Table 4 · p. 7, §4.2 · p. 7
  • AVE surpasses strong closed-source systems on swap/change, remove, and averaged instruction following and preservation, while those systems retain a slight edge on add and style/tone.AVE’s overall quality score is 8.630, on par with or slightly better than the strongest open-source baselines.Source: §4.2 · p. 7
  • Qualitative examples span appearance, scene, object, and motion edits, with appearance edits preserving original motion and substantial motion changes remaining smooth.Examples include background removal, viewpoint change, object relocation, style transfer, local attribute editing, and motion modification.Source: §4.2 · pp. 7–8, Figure 3 · p. 8

4.3 Ablation Study

  • Final video editing quality strongly correlates with Stage-1 image-editor quality; tuned BAGEL also significantly outperforms untuned BAGEL.With Stage 2 fixed, replacing the default editor with other strong closed-source editors causes only a mild drop, while untuned BAGEL causes a much larger degradation.Source: §4.3 · p. 8, Table 5 · p. 9
  • AVE is fairly robust to keyframe selection, though scene-aware selection consistently beats uniform sampling, especially with limited budgets.The default strategy combines scene-aware anchors from PySceneDetect with largest-gap completion.Source: Table 6 · p. 9, §4.3 · p. 9
  • Performance is best with approximately ⌊T/24⌋ keyframes; too few weaken instruction compliance and fidelity, while too many slightly hurt performance.The authors hypothesize that overly dense anchors reduce the effective interpolation space after Wan2.2’s 4× temporal downsampling.Source: §4.3 · p. 9

5 Conclusion

AVE transforms image editors into video editors by editing selected keyframes and using motion-guided image-to-video diffusion to interpolate the full video. The paper reports strong performance across several editing dimensions and a correlation between video-editing quality and image-editor quality.

5 Conclusion

  • AVE edits selected keyframes with a powerful image editor, then uses motion-guided image-to-video diffusion to interpolate the full edited video.The framework is designed to inherit modern image editors’ strengths while avoiding expensive end-to-end video-editing training.Source: §5 · p. 9
  • Experiments on IVEBench and VIE-Bench show strong performance in instruction following, semantic consistency, content fidelity, and motion preservation.Source: §5 · p. 9
  • Ablations reveal that final video-editing quality is strongly correlated with image-editor quality.Source: §5 · p. 9

Formal Details of The Motion Encoder

The motion encoder processes each layer with spatial and temporal attention, then a feed-forward network. Its final representation is injected into a frozen video diffusion model to preserve source motion while edited anchors determine appearance and editing semantics.

Formal Details of The Motion Encoder

Training Stage-2

Stage 2 training keeps the Wan 3D VAE and DiT frozen while optimizing the motion encoder and ControlNet-style residual branch. The training setup replaces selected anchors with clean latents and computes loss only on non-anchor frames, teaching interpolation between fixed anchors.

Training Stage-2

  • Training keeps the Wan 3D VAE and DiT frozen and optimizes only the motion encoder and ControlNet-style residual branch.Source: Training Stage-2 · p. 13
  • The training process samples a diffusion timestep and noise to obtain a noisy latent video, then replaces selected anchor frames with their clean latent values.Source: Training Stage-2 · p. 13
  • The model predicts a noise residual and computes training loss only on non-anchor frames.Source: Training Stage-2 · p. 13
  • This objective trains the model to interpolate and propagate content between fixed anchors rather than re-synthesizing the anchors.Source: Training Stage-2 · p. 13

Broader Impacts

The paper frames its work as foundational research in video generation and reports no specific direct societal impacts beyond general considerations associated with generative media.

Broader Impacts

  • The work focuses on foundational research in video generation.Source: Broader Impacts · p. 13
  • The authors did not identify specific direct societal impacts beyond general considerations accompanying generative media.Source: Broader Impacts · p. 13
  • The stated impact scope is limited to general considerations associated with generative media.Source: Broader Impacts · p. 13

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