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VEBench benchmark evaluates large multimodal models for video editing tasks

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have introduced VEBENCH, a new benchmark designed to evaluate Large Multimodal Models (LMMs) in real-world video editing tasks. The benchmark includes over 3.9K edited videos and 3,080 question-answer pairs, focusing on recognizing editing techniques and simulating editing workflows. Experiments using VEBENCH revealed a significant performance gap between current LMMs and human capabilities in video editing, highlighting the need for improved multimodal reasoning and operational capabilities. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Establishes a new standard for evaluating AI in video editing, potentially guiding future development of more capable creative AI tools.

RANK_REASON This is a research paper introducing a new benchmark for evaluating AI models.

Read on arXiv cs.CV →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 · Andong Deng, Dawei Du, Zhenfang Chen, Wen Zhong, Fan Chen, Guang Chen, Chia-Wen Kuo, Longyin Wen, Chen Chen, Sijie Zhu ·

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

    arXiv:2605.03276v1 Announce Type: new 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 …

  2. arXiv cs.CV TIER_1 · Sijie Zhu ·

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

    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 understandi…

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