VEBench:Benchmarking Large Multimodal Models for Real-World Video Editing

TL;DR

VEBENCH is a comprehensive benchmark for evaluating large multimodal models' abilities in real-world video editing tasks, focusing on editing knowledge and operational reasoning.

Contribution

It introduces VEBENCH, a new benchmark with high-quality videos and QA tasks to assess models' editing techniques recognition and workflow reasoning capabilities.

Findings

Current models lag behind human-level editing cognition.

Extensive experiments show significant performance gaps.

VEBENCH provides a foundation for future research in video editing AI.

Abstract

Real-world video editing demands not only expert knowledge of cinematic techniques but also multimodal reasoning to select, align, and combine footage into coherent narratives. While recent Large Multimodal Models (LMMs) have shown remarkable progress in general video understanding, their abilities in multi-video reasoning and operational editing workflows remain largely unexplored. We introduce VEBENCH, the first comprehensive benchmark designed to evaluate both editing knowledge understanding and operational reasoning in realistic video editing scenarios. VEBENCH contains 3.9K high-quality edited videos (over 257 hours) and 3,080 human-verified QA pairs, built through a three-round human-AI collaborative annotation pipeline that ensures precise temporal labeling and semantic consistency. It features two complementary QA tasks: 1) Video Editing Technique Recognition, assessing models' ability to identify 7 editing techniques using multimodal cues; and 2) Video Editing Operation Simulation, modeling real-world editing workflows by requiring the selection and temporal localization of relevant clips from multiple candidates. Extensive experiments across proprietary (e.g., Gemini-2.5-Pro) and open-source LMMs reveal a large gap between current model performance and human-level editing cognition. These results highlight the urgent need for bridging video understanding with creative operational reasoning. We envision VEBENCH as a foundation for advancing intelligent video editing systems and driving future research on complex reasoning.