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.

翻译：指令引导视频编辑已成为一个快速发展的研究方向，在提供直观内容转换新机遇的同时，也为系统性评估带来了重大挑战。现有视频编辑基准无法充分支持指令引导视频编辑的评估，并进一步受限于源视频多样性不足、任务覆盖范围狭窄及评估指标不完整等问题。为应对上述局限，本文提出IVEBench——一个专为指令引导视频编辑评估设计的现代基准测试套件。IVEBench构建了包含600个高质量源视频的多样化数据库，涵盖七个语义维度，视频长度范围覆盖32至1,024帧。该基准进一步包含8大类编辑任务及35个子类别，其指令提示通过大语言模型生成并经专家评审优化。关键的是，IVEBench建立了涵盖视频质量、指令遵循度和视频保真度的三维评估体系，整合了传统指标与基于多模态大语言模型的评估方法。大量实验证明IVEBench在评估当前最先进的指令引导视频编辑方法方面具有显著效果，能够提供全面且符合人类感知的评估结果。