IVEBench: Modern Benchmark Suite for Instruction-Guided Video Editing Assessment
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911547643658240 |
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| author | Chen, Yinan Zhang, Jiangning Hu, Teng Zeng, Yuxiang Xue, Zhucun He, Qingdong Wang, Chengjie Liu, Yong Hu, Xiaobin Yan, Shuicheng |
| author_facet | Chen, Yinan Zhang, Jiangning Hu, Teng Zeng, Yuxiang Xue, Zhucun He, Qingdong Wang, Chengjie Liu, Yong Hu, Xiaobin Yan, Shuicheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11647 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IVEBench: Modern Benchmark Suite for Instruction-Guided Video Editing Assessment Chen, Yinan Zhang, Jiangning Hu, Teng Zeng, Yuxiang Xue, Zhucun He, Qingdong Wang, Chengjie Liu, Yong Hu, Xiaobin Yan, Shuicheng Computer Vision and Pattern Recognition 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. |
| title | IVEBench: Modern Benchmark Suite for Instruction-Guided Video Editing Assessment |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.11647 |