Current video editing models often rely on expensive paired video data, which limits their practical scalability. In essence, most video editing tasks can be formulated as a decoupled spatiotemporal process, where the temporal dynamics of the pretrained model are preserved while spatial content is selectively and precisely modified. Based on this insight, we propose ImVideoEdit, an efficient framework that learns video editing capabilities entirely from image pairs. By freezing the pre-trained 3D attention modules and treating images as single-frame videos, we decouple the 2D spatial learning process to help preserve the original temporal dynamics. The core of our approach is a Predict-Update Spatial Difference Attention module that progressively extracts and injects spatial differences. Rather than relying on rigid external masks, we incorporate a Text-Guided Dynamic Semantic Gating mechanism for adaptive and implicit text-driven modifications. Despite training on only 13K image pairs for 5 epochs with exceptionally low computational overhead, ImVideoEdit achieves editing fidelity and temporal consistency comparable to larger models trained on extensive video datasets.
Figures & tables
Figure 1: Illustration of four of ImVideoEdit ’s basic editing task. Zoom in for best viewing.
Figure 2: Overview of ImVideoEdit . Left: The overall pipeline processes latents from single image through a frozen 3D DiT, featuring a Predict-Update module parallel to each attention block. Right: Detailed design of the Predict & Update Module. The frozen 3D self-attention safeguards spatiotemporal priors, while the parallel 2D branch extracts spatial features from the reference latent. The Predict Module generates a coarse alignment ( Hpred ) via a Zero-Linear layer. The spatial difference ( Hres ) is then processed by the Update Module and a Text Gate, which implicitly modulates the editing intensity to yield Hdiff . The final output ( Hupdate ) achieves precise text-driven spatial reconstruction. During training stage, the numbers of frame F maintains 1 .
Figure 3: Overview of the dataset construction pipeline.
Figure 4: Dataset statistics.
Method
Bg. Rep.
Cam. Trans.
Color/Texture Trans.
Style Trans.
Relight
Local Edit
Rigid/Non Rep.
Obj. Add./Rem.
AVG.
13B & 14B Parameter Models
Vace (14B)
52.3
58.6
58.0
69.7
79.7
56.0
60.8
50.5
59.68
DITTO (14B)
57.7
61.8
60.6
74.7
78.6
50.1
62.0
55.4
61.82
ICVE (13B)
55.7
59.5
75.6
69.0
67.7
57.6
77.1
55.7
65.04
5B Parameter Models
Kiwi-Edit (5B)
46.8
64.4
81.9
69.4
87.4
67.4
81.3
65.3
71.13
Table 1: Quantitative Results of VLM across all subtasks. The best are highlighted in bold .
Method
Subject Consist. ↑
Background Consist. ↑
Motion Smooth. ↑
Dynamic Deg. ↑
Aesthetic Qual. ↑
Imaging Qual. ↑
13B & 14B Parameter Models
Vace(14B)
0.973
0.973
0.990
0.296
0.685
0.715
DITTO(14B)
0.979
0.968
0.994
0.140
0.655
0.670
ICVE(13B)
0.972
0.959
0.991
0.404
0.629
0.697
5B Parameter Models
Kiwi-Edit(5B)
0.976
0.959
0.993
0.140
0.616
0.716
Table 2: Quantitative Results on VBench. The best are highlighted in bold .
Figure 5: Qualitative Results of ImVideoEdit and baselines.
Method
IA ↑
TC ↑
VF ↑
AA ↑
Total ↑
13B & 14B Parameter Models
Vace (14B)
10.67
23.99
14.41
10.62
59.68
DITTO (14B)
14.14
23.40
13.88
10.40
61.82
ICVE (13B)
15.64
22.85
16.04
10.51
65.04
5B Parameter Models
Kiwi-Edit (5B)
19.15
23.87
16.86
11.26
71.13
Table 3: Quantitative Results of VLM. Metrics: IA (Instruction Adherence), TC (Temporal Consistency), VF (Visual Fidelity) and AA (Artifact Absence).
Method
IA ↑
TC ↑
VF ↑
AA ↑
Total ↑
w/o Text Gate
15.29
20.37
14.31
9.76
59.73
w/o Update Module
9.71
18.10
11.09
7.59
47.29
Naive Parallel 2D (ViFedit [ 43 ] )
12.04
16.82
12.24
7.72
48.81
ImVideoEdit (Ours)
16.21
21.93
16.32
10.78
65.24
Table 4: Quantitative Ablation Results.
Figure 6: Qualitative Ablation Results.
Method
Overall Editing Quality ↑
VACE (1.3B)
2.18±0.17
Kiwi-Edit (5B)
3.08±0.13
ImVideoEdit (1.3B Based)
3.06±0.11
Table 5: User Study Results. ± denotes the standard deviation.
Appendix figures & tables8 assets
Supplementary material from the paper’s appendix.
Appendix
Evaluator Model
Pearson r↑
MAE ↓
Variance ↓
GPT-5.4
0.720
0.185
0.113
Gemini-2.5-Pro
0.843
0.137
0.068
Appendix
Table 6: Quantitative Cross-Validation of VLM Evaluators. We report the linear correlation (Pearson r ), Mean Absolute Error (MAE), and Inter-model Variance of various models against Gemini-3.1-Pro (the default evaluator in our pipeline).
Prompt Template
# Role You are an expert video quality assessor and professional video editor. Your task is to evaluate the quality of an AI-edited video based on a specific user instruction, comparing it to the original unedited video. # Inputs ∙ Original Video: [Insert pre-edited video] ∙ Edited Video: [Insert post-edited video] ∙ Editing Instruction: ”{Insert the editing instruction here}” # Evaluation Criteria Please evaluate the edited video rigorously based on the following four dimensions. Provide a distinct score for each dimension based on its maximum point value. 1. Instruction Adherence (Max: 30 points): ∙ Did the editing strictly follow the given instruction? ∙ Are the requested changes accurately reflected without altering unintended elements? 2. Temporal Consistency & Micro-Stability (Max: 30 points) - STRICT DEDUCTION RULES : ∙ VLM Warning: Do not just look at the overall subject. You must zoom in on high-frequency details. ∙ Scoring Anchor: - 28-30 pts: Perfect stability, identical to the physics of a real camera. - 20-27 pts: Overall stable, but minor ”AI boiling” (micro-flickering of pixels) on edges or complex patterns during movement. - 10-19 pts: Noticeable morphing, shifting of textures (e.g., embroidery changing shape frame-by-frame), or jittery outlines. - 0-9 pts: Severe flickering or structural collapse. 3. Texture Sharpness & Anti-Smoothing (Max: 25 points) - CALIBRATED FOR AI : ∙ Do not compare this to an 8K cinema camera. Evaluate the AI rendering quality. We are looking for SHARPNESS vs. PLASTICITY. ∙ Focus on the materials: the velvet texture of the blue dress, the individual threads of the embroidery, and the natural pores/lighting on the skin. ∙ Scoring Anchor: - 21-25 pts: Excellent sharpness. Fabrics look like real cloth with distinct threads. Lighting has depth and natural contrast. - 15-20 pts: Good, but slightly soft. - 10-14 pts: The ”Plastic AI” look. Textures are overly smoothed, faces look like wax or airbrushed, and fine details (like embroidery) look like flat paint rather than raised threads. (DEDUCT HEAVILY HERE). - 0-9 pts: Extremely blurry or washed out. 4. Artifact Absence (Max: 15 points): ∙ Are there any visible AI generation artifacts (e.g., floating pixels, anatomical distortions, weird edge blending)? ∙ The edited areas should blend seamlessly with the original unedited parts. # Output Format Provide your response strictly in the following JSON format. Do not include a total score, only the sub-scores for each dimension. { ”Reasoning”: ”Provide a concise step-by-step analysis evaluating the video against the 4 criteria. Explicitly mention your observations on temporal stability and texture details.”, ”Scores”: { ”Instruction_Adherence”: <int between 0 and 30>, ”Temporal_Consistency”: <int between 0 and 30>, ”Visual_Fidelity”: <int between 0 and 25>, ”Artifact_Absence”: <int between 0 and 15> } }
Appendix
Table 7: System Prompt for VLM Evaluation.
Task
Method
IA ↑
TC ↑
VF ↑
AA ↑
Total ↑
Background Replacement
VACE (14B)
4.4
25.6
12.2
10.0
52.3
DITTO (14B)
10.1
24.3
12.6
10.7
57.7
ICVE (13B)
7.3
22.9
15.7
9.8
55.7
Kiwi-Edit (5B)
8.0
18.4
12.8
7.5
46.8
Lucy-Edit-Dev (5B)
5.1
15.5
8.7
5.4
34.8
OmniVideo2 (1.3B)
4.4
12.0
9.1
5.0
30.5
Appendix
Table 8: Fine-grained VLM evaluation results for the first five task categories. IA, TC, VF, and AA denote Instruction Adherence, Temporal Consistency, Visual Fidelity, and Artifact Absence, respectively.
Task
Method
IA ↑
TC ↑
VF ↑
AA ↑
Total ↑
Local Attribute/State Editing
VACE (14B)
7.2
25.4
13.0
10.5
56.0
DITTO (14B)
9.8
20.8
11.6
7.9
50.1
ICVE (13B)
9.4
23.8
14.6
9.8
57.6
Kiwi-Edit (5B)
15.8
24.6
15.8
11.2
67.4
Lucy-Edit-Dev (5B)
8.5
18.3
11.3
6.6
44.8
OmniVideo2 (1.3B)
10.0
16.9
12.3
7.5
46.7
Appendix
Table 9: Fine-grained VLM evaluation results for the remaining five task categories and the overall average. IA, TC, VF, and AA denote Instruction Adherence, Temporal Consistency, Visual Fidelity, and Artifact Absence, respectively.
Figure 7: visualizations of datasets (Part 1).
Figure 8: visualizations of datasets (Part 2).
Figure 9: Additional qualitative result (Part 1).
Figure 10: Additional qualitative result (Part 2).
Text-to-image diffusion models have advanced video editing. However, existing video editing methods are limited by their high computational overhead and memory consumption. Furthermore, these approaches often sacrifice visual fidelity, leading to temporal inconsistencies and artifacts such as blurring and mosaic-like patterns. To address these dual challenges and strike a balance between computational efficiency and visual fidelity, we propose Edit-Your-Interest, a lightweight, text-driven, zero-shot video editing method. Edit-Your-Interest introduces a Spatio-Temporal Feature Memory bank (SFM) to cache spatial-attention features from previous frames under a fixed memory budget. The SFM maintains a representative candidate pool, reducing the computational overhead of full-sequence spatio-temporal modeling. To query this memory, we propose Feature Most-Similar Propagation (FMP), a similarity-based retrieval rule that propagates relevant cached tokens to the current frame. Together, SFM and FMP form a bounded-memory retrieval-based propagation design that improves temporal consistency while reducing blurring and mosaic-like artifacts. Finally, we introduce a feature-diversity-based SFM update algorithm to keep the memory representative and non-redundant throughout the video sequence. Furthermore, to enable precise object editing, we leverage cross-attention maps to automatically extract masks for the instances of interest. These masks are seamlessly integrated into the diffusion denoising process, enabling fine-grained control over target objects and allowing Edit-Your-Interest to perform highly accurate edits while robustly preserving the background integrity. Extensive experiments demonstrate that Edit-Your-Interest outperforms state-of-the-art methods in both efficiency and visual fidelity. Project page: https://edit-your-interest.github.io/.
Yi Zuo, Zitao Wang, Lingling Li +3
Xidian University,Xi’an, 710071, Shaanxi Province, China
Video editing aims to modify input videos according to user intent. Recently, end-to-end training methods have garnered widespread attention, constructing paired video editing data through video generation or editing models. However, compared to image editing, the high annotation costs of video data severely constrain the scale, quality, and task diversity of video editing datasets when relying on video generative models or manual annotation. To bridge this gap, we propose LIVE, a joint training framework that leverages large-scale, high-quality image editing data alongside video datasets to bolster editing capabilities. To mitigate the domain discrepancy between static images and dynamic videos, we introduce a frame-wise token noise strategy, which treats the latents of specific frames as reasoning tokens, leveraging large pretrained video generative models to create plausible temporal transformations. Moreover, through cleaning public datasets and constructing an automated data pipeline, we adopt a two-stage training strategy to anneal video editing capabilities. Furthermore, we curate a comprehensive evaluation benchmark encompassing over 60 challenging tasks that are prevalent in image editing but scarce in existing video datasets. Extensive comparative and ablation experiments demonstrate that our method achieves state-of-the-art performance. The source code will be publicly available.
Weicheng Wang, Zhicheng Zhang, Zhongqi Zhang +6
Nankai University · Kuaishou Technology · Pengcheng Laboratory +1
Video editing spans diverse editing paradigms, yet achieving high-quality instruction-guided and subject-guided editing within a single unified framework remains challenging. We introduce EditVid, a training-free framework combining sparse causal memory for local coherence, correspondence-based post-attention token injection for long-range identity preservation, and soft latent blending for edit locality. The same framework supports instruction-guided and reference-guided edits, including style transfer, attribute modification, object insertion, part-level editing, and subject replacement. On FiVE, EditVid achieves 78.16 FiVE-Acc, compared with 58.95 for the strongest evaluated training-free baseline, while obtaining competitive results on IVEBench. A user study further shows a 51.8% overall preference for EditVid over 7 competing methods.
Adheesh Sunil Juvekar, Onkar Kishor Susladkar, Kiet A. Nguyen +6