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

arXiv: https://arxiv.org/abs/2510.11647 · HTML: https://mlsanity.com/paper/2510.11647/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 42 references that are in the map.
- First posted on arXiv 13 Oct 2025.

## Builds on (most cited first, 30 of 37)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2212.11565/) (2022)
- [Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://mlsanity.com/paper/2303.13439/) (2023)
- [VACE: All-in-One Video Creation and Editing](https://mlsanity.com/paper/2503.07598/) (2025)
- [FateZero: Fusing Attentions for Zero-shot Text-based Video Editing](https://mlsanity.com/paper/2303.09535/) (2023)
- [TokenFlow: Consistent Diffusion Features for Consistent Video Editing](https://mlsanity.com/paper/2307.10373/) (2023)
- [Pix2Video: Video Editing using Image Diffusion](https://mlsanity.com/paper/2303.12688/) (2023)
- [T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation](https://mlsanity.com/paper/2407.14505/) (2024)
- [AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation](https://mlsanity.com/paper/2304.09790/) (2023)
- [FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing](https://mlsanity.com/paper/2310.05922/) (2023)
- [Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning](https://mlsanity.com/paper/2504.13914/) (2025)
- [AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks](https://mlsanity.com/paper/2403.14468/) (2024)
- [Koala-36M: A Large-scale Video Dataset Improving Consistency between Fine-grained Conditions and Video Content](https://mlsanity.com/paper/2410.08260/) (2024)
- [Space-Time Diffusion Features for Zero-Shot Text-Driven Motion Transfer](https://mlsanity.com/paper/2311.17009/) (2023)
- [RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models](https://mlsanity.com/paper/2312.04524/) (2023)
- [VidToMe: Video Token Merging for Zero-Shot Video Editing](https://mlsanity.com/paper/2312.10656/) (2023)
- [UniVideo: Unified Understanding, Generation, and Editing for Videos](https://mlsanity.com/paper/2510.08377/) (2025)
- [Consistent Video-to-Video Transfer Using Synthetic Dataset](https://mlsanity.com/paper/2311.00213/) (2023)
- [OpenHumanVid: A Large-Scale High-Quality Dataset for Enhancing Human-Centric Video Generation](https://mlsanity.com/paper/2412.00115/) (2024)
- [InsViE-1M: Effective Instruction-based Video Editing with Elaborate Dataset Construction](https://mlsanity.com/paper/2503.20287/) (2025)
- [Ground-A-Video: Zero-shot Grounded Video Editing using Text-to-image Diffusion Models](https://mlsanity.com/paper/2310.01107/) (2023)
- [UltraVideo: High-Quality UHD Video Dataset with Comprehensive Captions](https://mlsanity.com/paper/2506.13691/) (2025)
- [VideoCLIP-XL: Advancing Long Description Understanding for Video CLIP Models](https://mlsanity.com/paper/2410.00741/) (2024)

## Built on by (most cited first, 28 of 28)

- [UltraGen: High-Resolution Video Generation with Hierarchical Attention](https://mlsanity.com/paper/2510.18775/) (2025)
- [RULER-Bench: Probing Rule-based Reasoning Abilities of Next-level Video Generation Models for Vision Foundation Intelligence](https://mlsanity.com/paper/2512.02622/) (2025)
- [PISCO: Precise Video Instance Insertion with Sparse Control](https://mlsanity.com/paper/2602.08277/) (2026)
- [EgoEdit: Dataset, Real-Time Streaming Model, and Benchmark for Egocentric Video Editing](https://mlsanity.com/paper/2512.06065/) (2025)
- [PAI-Studio: Cinematic Video Background Replacement with Camera-Aware Motion](https://mlsanity.com/paper/2606.01399/) (2026)
- [Advancing Narrative Long Video Generation via Training-Free Identity-Aware Memory](https://mlsanity.com/paper/2605.18733/) (2026)
- [AgenticVBench: Can AI Agents Complete Real-World Post-Production Tasks?](https://mlsanity.com/paper/2605.27705/) (2026)
- [PoseAnything: Universal Pose-guided Video Generation with Part-aware Temporal Coherence](https://mlsanity.com/paper/2512.13465/) (2025)
- [FFP-300K: Scaling First-Frame Propagation for Generalizable Video Editing](https://mlsanity.com/paper/2601.01720/) (2026)
- [VideoArgus: Agentic Rubric-Grounded Unified Evaluation for Video Generation and Editing](https://mlsanity.com/paper/2608.05485/) (2026)
- [VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects](https://mlsanity.com/paper/2604.16272/) (2026)
- [LIVEditor-14B: Lightning Unified Video Editing via In-Context Sparse Attention](https://mlsanity.com/paper/2605.04569/) (2026)
- [Physics-Aware Video Instance Removal Benchmark](https://mlsanity.com/paper/2604.05898/) (2026)
- [CoVEBench: Can Video Editing Models Handle Complex Instructions?](https://mlsanity.com/paper/2606.08415/) (2026)
- [ViDiC: Video Difference Captioning](https://mlsanity.com/paper/2512.03405/) (2025)
- [JAVEDIT: Joint Audio-Visual Instruction-Guided Video Editing with Agentic Data Curation](https://mlsanity.com/paper/2606.03168/) (2026)
- [GRNEdit: Efficient General Video Editing from a New Binary-Evidence Perspective in Generative Refinement Networks](https://mlsanity.com/paper/2608.16328/) (2026)
- [LIVE: Leveraging Image Manipulation Priors for Instruction-based Video Editing](https://mlsanity.com/paper/2604.17021/) (2026)
- [CutVerse: A Compositional GUI Agents Benchmark for Media Post-Production Editing](https://mlsanity.com/paper/2605.19484/) (2026)
- [AVE-Compass: Towards Holistic Evaluation for Audio-Video Editing Abilities](https://mlsanity.com/paper/2607.24821/) (2026)
- [OmniEdit-Bench: A Comprehensive Benchmark for Instruction-based Video Editing](https://mlsanity.com/paper/2608.05049/) (2026)
- [What Semantics Survive the Connector? Diagnosing VLM-to-DiT Alignment in Video Editing](https://mlsanity.com/paper/2605.20795/) (2026)
- [Vera: A Layered Diffusion Model for Content-Preserving Video Editing](https://mlsanity.com/paper/2606.23610/) (2026)
- [VicEdit: Learning to Edit Videos from Visual In-Context Examples](https://mlsanity.com/paper/2608.16745/) (2026)
- [One Editor, Many Edits: A Unified Training-Free Framework for Diverse Video Editing](https://mlsanity.com/paper/2609.04190/) (2026)
- [AVENUE: Audio-Video EditiNg Understanding and Evaluation](https://mlsanity.com/paper/2609.04253/) (2026)
- [ViTeX-Bench: Benchmarking High-Fidelity Video Scene Text Editing](https://mlsanity.com/paper/2609.40356/) (2026)
- [MotionCoach: Constructing Multimodal Guidance for Video Professional Motion Editing](https://mlsanity.com/paper/2610.12963/) (2026)

## Abstract (arXiv)

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.