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IVEBench: Modern Benchmark Suite for Instruction-Guided Video Editing Assessment

Yinan Chen, Jiangning Zhang, Teng Hu, Yuxiang Zeng, Zhucun Xue, Qingdong He, Chengjie Wang, Yong Liu, Xiaobin Hu, Shuicheng Yan

arXiv:2510.11647v2cs.CV

TL;DR

Existing benchmarks inadequately evaluate instruction-guided video editing because they lack sufficient source diversity, task coverage, and comprehensive evaluation dimensions. IVEBench addresses these gaps with a diverse video benchmark and multidimensional evaluation suite, which shows high alignment with human judgments.

  • Problem

    Existing video editing benchmarks have limited source diversity, restricted editing prompts, and incomplete evaluation dimensions, creating a need for comprehensive instruction-guided video editing evaluation.

  • Method

    IVEBench combines semantically diverse source videos with broad editing evaluation and multidimensional quality assessment, including supervised video-quality indicators such as compositional coherence, sharpness, and motion stability.

  • Results

    IVEBench exhibits a high degree of alignment with human evaluation, supporting comprehensive assessment of instruction-guided video editing methods.

  • Takeaways & Limitations

    IVEBench provides a benchmark intended to support more meaningful evaluation and guide subsequent research on instruction-guided video editing.

  • Takeaways & Limitations

    First-frame-based editing models are inadequate for editing requirements involving transitions or intermediate events that occur later in a video.

Abstract

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Instruction-guided video editing has emerged as a rapidly advancing research direction, offering new opportunities for intuitive content transformation while also posing significant challenges for systematic evaluation. Existing video editing benchmarks fail to support the evaluation of instruction-guided video editing adequately and further suffer from limited source diversity, narrow task coverage and incomplete evaluation metrics. To address the above limitations, we introduce IVEBench, a modern benchmark suite specifically designed for instruction-guided video editing assessment. IVEBench comprises a diverse database of 600 high-quality source videos, spanning seven semantic dimensions, and covering video lengths ranging from 32 to 1,024 frames. It further includes 8 categories of editing tasks with 35 subcategories, whose prompts are generated and refined through large language models and expert review. Crucially, IVEBench establishes a three-dimensional evaluation protocol encompassing video quality, instruction compliance and video fidelity, integrating both traditional metrics and multimodal large language model-based assessments. Extensive experiments demonstrate the effectiveness of IVEBench in benchmarking state-of-the-art instruction-guided video editing methods, showing its ability to provide comprehensive and human-aligned evaluation outcomes.

1 Introduction

IVEBench addresses the limited diversity, task coverage, and evaluation dimensions of existing video editing benchmarks for instruction-guided video editing. It combines a diverse corpus, comprehensive prompts, and human-aligned evaluation to support systematic assessment.

  • Existing benchmarks inadequately evaluate instruction-guided video editing because they have limited source diversity, narrow prompts, and incomplete metrics.
  • IVEBench introduces 600 high-quality source videos organized across seven semantic dimensions, alongside prompts spanning eight editing categories and 35 subcategories.
  • Its three-dimensional protocol evaluates video quality, instruction compliance, and video fidelity using traditional metrics and multimodal large language model assessments.
  • The evaluation suite demonstrates high alignment with human perception across all metrics and provides qualitative and quantitative analyses of mainstream instruction-guided video editing methods.

2 Related Work

Instruction-guided video editing extends earlier source-target prompt approaches toward more user-friendly natural-language control. Related benchmarks and methods have advanced the field but leave coverage and evaluation gaps that IVEBench targets.

  • Instruction-guided approaches gained attention because users express editing requirements through instructions rather than detailed target prompts.
  • Earlier source-target prompt methods preserve object locations and poses during inversion but are limited for subject movement and camera motion.
  • Recent methods increasingly combine multimodal language understanding with diffusion-based generation or editing in unified architectures.
  • Dedicated video-editing benchmarks such as VE-Bench and EditBoard introduced datasets and evaluation systems for text-driven editing.

3 IVEBENCH Database

IVEBench builds a diverse and structured database from curated videos, fine-grained semantic annotations, and broad editing objectives. Its prompt pipeline generates category-specific editing and target prompts for evaluation.

  • 3.1 Diverse Video Collection for IVE: The database collects high-quality videos across seven semantic dimensions and 30 fine-grained topics from multiple public and open-source sources.
  • 3.1 Diverse Video Collection for IVE: A hybrid automated and manual filtering pipeline removes black borders, subtitles, and low-quality content before screening videos for editing suitability.
  • 3.2 Comprehensive Editing Prompts: Structural captions describe subjects, backgrounds, actions, atmosphere, styles, and camera properties to form a vocabulary of editable elements.
  • 3.2 Comprehensive Editing Prompts: Editing objectives span eight major categories and 35 subcategories to broaden task coverage beyond existing benchmarks.
  • 3.2 Comprehensive Editing Prompts: Doubao-1.5-pro selects an editing category and generates an edit prompt, target prompt, and target phrase for subsequent evaluation.

4 Comprehensive Metrics of IVEBENCH

IVEBench evaluates edited videos across video quality, instruction compliance, and fidelity, combining conventional metrics with multimodal-model assessments and human-alignment validation.

  • 4 Comprehensive Metrics of IVEBENCH: IVEBench evaluates each editing instance across video quality, instruction compliance, and fidelity.Video quality concerns the target video, instruction compliance its alignment with the edit prompt, and fidelity consistency with the source video.
  • 4.1 Video Quality: Video quality covers temporal continuity, spatial quality, subject and background consistency, flickering, and motion smoothness.VTSS integrates compositional coherence, aesthetics, sharpness, color saturation, naturalness, and motion stability.
  • 4.2 Instruction Compliance: Instruction compliance combines semantic-consistency metrics, multimodal instruction satisfaction, and task-specific quantity accuracy.VideoCLIP-XL2 measures overall and phrase-level consistency, Qwen2.5-VL scores prompt execution, and Grounding DINO checks requested quantities.
  • 4.3 Fidelity: Fidelity measures whether edited videos preserve unedited source content through semantic, motion, and multimodal content-fidelity assessments.VideoCLIP-XL2 measures source-target semantic similarity, while Cotracker3 extracts motion trajectories and Qwen2.5-VL evaluates retained content.
  • 4 Comprehensive Metrics of IVEBENCH: The benchmark uses 12 metrics, including four adopted from VBench, and independence tests support high metric independence.SC, BC, TF, and MS are adopted from VBench; the remaining eight metrics underwent independence testing.
  • 4.4 Human Alignment for Benchmark Validation: Human validation compares outputs pairwise across evaluation dimensions using trained annotators, while metric weights derive from average contribution ratings.Three models produce pairwise comparisons on 30 source videos, and annotators can select the better video or mark pairs hard to distinguish.

5 Discussion with Recent Video Editing Benchmarks

Existing video editing benchmarks provide limited support for instruction-guided methods and insufficient coverage of video-specific editing requirements.

  • 5 Discussion with Recent Video Editing Benchmarks: Existing benchmarks mainly target source-target prompt methods and offer limited support for instruction-guided video editing.Their prompt designs largely remain restricted to image-editing types such as subject, attribute, and style editing, without dedicated temporal video-task formulations.

6 Benchmarking Video Editing Method in IVEBench

IVEBench evaluates eight instruction-guided video editing models across quality, compliance, fidelity, efficiency, and human-alignment dimensions. Results reveal strong temporal consistency but weak frame quality, instruction adherence, and support for challenging edits.

  • Quantitative analysis: All eight methods preserve frame-to-frame consistency relatively well, but their Total Scores remain at or below 0.7.Per-frame artifacts contribute to low Video Fidelity, while limited task support constrains instruction adherence.
  • Quantitative analysis: Ditto and InsV2V achieve the strongest editing capability, while Ditto attains the best Instruction Compliance score.Lucy-Edit-Dev performs best in editing speed, and Ditto handles more cases than the other evaluated methods.
  • Qualitative analysis: Qualitative comparisons expose localization errors, geometric distortion, semantic bleeding, boundary blurring, and texture flickering across models.These artifacts reduce per-frame visual quality and overall video fidelity.
  • Insights and discussions: Models perform poorly on advanced tasks such as subject motion and camera angle editing, indicating limited coverage beyond basic editing types.Supported basic categories include subject, style, and attribution editing, whereas quantity, motion, visual-effect, camera-motion, and camera-angle tasks remain difficult.
  • Insights and discussions: First-frame-based models are inadequate for edits involving transitions or events introduced in middle or later frames.These models propagate changes from the beginning through the entire video rather than modifying later temporal regions directly.
  • Insights and discussions: Most existing methods become impractical beyond 128 frames, whereas InsV2V limits memory growth through chunked inference with latent overlap.Existing methods often show near-linear growth in GPU memory and latency as sequence length increases.

7 Conclusion

The paper presents IVEBench as a comprehensive benchmark addressing gaps in source diversity, task coverage, and evaluation dimensions for instruction-guided video editing. Future work will expand model coverage and evaluation data as resources increase.

  • Conclusion: Existing benchmarks insufficiently reflect current methods because they lack source diversity, task coverage, and comprehensive evaluation dimensions.The paper identifies systematic evaluation as a central challenge in the field.
  • Conclusion: IVEBench integrates a diverse dataset, broad editing tasks, and a multidimensional protocol using MLLMs aligned with human perception.The benchmark is intended to provide more reliable evaluation and guidance for instruction-guided video editing research.
  • Limitation and future work: Future IVEBench releases will incorporate additional open-source models and expand the evaluation-data scale as computational power increases.These extensions are presented as planned future work rather than completed benchmark capabilities.

Ethics statement

The dataset uses publicly available, open-licensed videos and follows privacy, fairness, transparency, and responsible-stewardship commitments. Data and code are released solely for academic research.

  • Ethics statement: The dataset is built from publicly available, open-licensed video sources, with annotations conducted in accordance with privacy and fairness considerations.The authors state that no personally identifiable or sensitive information is included.
  • Ethics statement: The authors release data and code to promote transparency and reproducibility in instruction-guided video editing research.The release is restricted to advancing academic research.

Reproducibility Statement

IVEBench documents its prompt taxonomy, motion-fidelity computation, unified scoring, evaluation setup, and supplementary reproducibility materials. The benchmark covers diverse editing operations and evaluates video quality, instruction compliance, and fidelity.

  • Reproducibility Statement: The supplementary material documents prompt subcategories, motion-fidelity computation, unified scoring, experimental details, model comparisons, human alignment, metric independence, and LLM use.It also states that code and the dataset will be released.
  • Prompt taxonomy: IVEBench defines 35 editing-prompt subcategories with descriptions and representative examples, spanning subject, attribute, motion, camera, angle, style, and weather edits.The prompt collection is intended to support clarity, reproducibility, and broad task coverage.
  • Motion fidelity: Motion fidelity matches source and target trajectories with the Hungarian algorithm after synchronizing videos to the shorter length and discarding correspondences with similarity at most 0.3.The dataset-level score averages motion-fidelity values across video pairs.
  • Unified scoring: Each evaluation dimension aggregates weighted metrics, while the total score aggregates the three dimension scores using user-study-derived weights.Video Quality, Instruction Compliance, and Video Fidelity receive equal dimension weights, while metric weights include VTSS=5, IS=3, and CF=3.
  • Experimental setup: Experiments evaluate short and long video subsets with twelve indicators across three dimensions, recording runtime and peak GPU memory while documenting failed long-sequence cases.Outputs use each model’s recommended resolution and match the input frame count when processing succeeds.

E Detailed quantitative comparison and analysis

The detailed comparison examines model behavior across editing categories, frame lengths, and qualitative visualizations. Results show distinct trade-offs among balanced, conservative, aggressive, and non-native editing strategies.

  • Qualitative analysis: Concatenating first, middle, and last frames exposes temporal dynamics and qualitative differences that scalar metrics may not fully capture.The human interface supports pairwise comparisons of model outputs under a specified evaluation dimension.
  • Model comparison: InsV2V maintains relatively balanced performance, with higher semantic and motion fidelity on longer sequences, but its conservative edits reduce instruction satisfaction.AnyV2V performs strongly on simpler style and attribute edits but struggles on difficult tasks.
  • Model comparison: StableV2V increases instruction satisfaction through aggressive editing but produces severe semantic bleeding and boundary artifacts on complex prompts.VACE provides temporal smoothness and high-resolution outputs, yet restricted frame length and weaker instruction compliance limit its applicability.
  • Overall findings: Overall, current models preserve frame-to-frame coherence but still struggle to execute diverse instructions faithfully while maintaining high per-frame fidelity.These findings motivate fine-grained benchmark analysis for future methodological improvement.

G Independence of Metrics

IVEBench evaluates whether its metrics are independent rather than redundant. Correlation analysis supports distinct metric behavior and reveals trade-offs among evaluated models.

  • Independence analysis: Spearman correlations among all eight IVEBench metrics do not exceed 0.65, indicating no collinearity and supporting metric independence.The analysis uses results from all evaluated models.
  • Interpretation: Negative correlations among several metrics suggest that the metric suite can reveal trade-offs among evaluated models.Metric independence is presented as important for avoiding redundancy and exposing different model behaviors.
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