IVEBench：现代指令引导视频编辑评估基准套件
IVEBench: Modern Benchmark Suite for Instruction-Guided Video Editing Assessment
AI总结：
IVEBench通过引入多样化的视频数据库和多维评估协议，解决现有视频编辑基准在评估指令引导视频编辑方面的不足，提供全面的人类对齐评估结果。
摘要：
指令引导视频编辑已成为快速发展的研究方向，为直观内容转换提供了新机会，但也对系统评估提出了重大挑战。现有视频编辑基准无法充分评估指令引导视频编辑，且存在来源单一、任务覆盖窄和评估指标不完整的问题。为解决上述限制，我们引入IVEBench，专门设计用于指令引导视频编辑评估。IVEBench包含600个高质量源视频数据库，涵盖七个语义维度，视频长度从32到1024帧不等。它进一步包括8类编辑任务和35个子类，其提示通过大型语言模型生成和专家审查进行优化。关键的是，IVEBench建立了一个三维评估协议，涵盖视频质量、指令合规性和视频保真度，整合了传统指标和多模态大语言模型评估。广泛实验表明，IVEBench在基准测试最新指令引导视频编辑方法方面表现出色，展示了其提供全面且人类对齐评估结果的能力。
英文摘要：
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.
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