Title: IVEBench: Modern Benchmark Suite for Instruction-Guided Video Editing Assessment
Primary area: datasets and benchmarks
Keywords: Instruction-guided video editing, Benchmark suite, Multimodal large language models, Evaluation metrics

Abstract: 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 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. All data and code will be made publicly available.

TL;DR: IVEBench is a large-scale benchmark for instruction-guided video editing with 600 videos, 7 dimensions, 8 task categories, and 35 subcategories, offering a 3D evaluation (quality, compliance, fidelity) with human-aligned, reproducible metrics.