VEBench: 多模态大模型在真实世界视频编辑中的基准测试
VEBench:Benchmarking Large Multimodal Models for Real-World Video Editing
AI总结：
VEBench通过高质量视频和人工验证问题对多模态大模型在视频编辑中的知识理解和操作推理能力进行评估，揭示当前模型与人类水平间的显著差距。
摘要：
真实世界视频编辑不仅需要电影技巧的专家知识，还需要多模态推理来选择、对齐和组合片段形成连贯的叙事。尽管最近的大型多模态模型（LMMs）在一般视频理解上取得了显著进展，但其在多视频推理和操作编辑流程中的能力仍 largely 未被探索。我们引入 VEBENCH，第一个全面的基准，旨在评估真实视频编辑场景中的编辑知识理解和操作推理能力。VEBENCH 包含 3,900 个高质量编辑视频（超过 257 小时）和 3,080 个人验证的 QA 对，通过三轮人机协作标注流程构建，确保精确的时间标记和语义一致性。它包含两个互补的 QA 任务：1）视频编辑技术识别，评估模型识别 7 种编辑技术的能力；2）视频编辑操作模拟，通过要求从多个候选片段中选择并定位时间片段来建模现实编辑流程。广泛的实验显示，当前模型性能与人类水平之间存在显著差距。这些结果突显了将视频理解与创造性操作推理结合的紧迫需求。我们设想 VEBENCH 作为推进智能视频编辑系统和未来复杂推理研究的基础。
英文摘要：
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.
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