EvalVerse: Pipeline-Aware and Expert-Calibrated Benchmarking for Professional Cinematic Video Generation
EvalVerse introduces a new evaluation framework for cinematic video generation that addresses the limitations of current benchmarks. It emphasizes the importance of assessing both the correctness and quality of generated videos, bridging the gap between human aesthetic perception and machine scoring. The framework incorporates expert knowledge and a structured evaluation taxonomy to enhance the evaluation process.
- ▪EvalVerse is designed to evaluate cinematic video generation beyond basic prompt-following metrics.
- ▪The framework organizes evaluation criteria according to the professional filmmaking workflow.
- ▪It incorporates human expert judgments into a curated dataset with large-scale annotations.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23271 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
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Computer Science > Computer Vision and Pattern Recognition arXiv:2605.23271 (cs) [Submitted on 22 May 2026] Title:EvalVerse: Pipeline-Aware and Expert-Calibrated Benchmarking for Professional Cinematic Video Generation Authors:Songlin Yang, Haobin Zhong, Ruilin Zhang, Xiaotong Zhao, Shuai Li, Kai Zheng, Xuyi Yang, Zhe Wang, Zhenchen Tang, Yang Li, Bohai Gu, Zhengwei Peng, Yidan Huang, Mengzhou Luo, Yihang Bo, Dalu Feng, Yujia Zhang, Juntao Ma, Ruiqi Wang, Lvmin Zhang, Yuwei Guo, Frank Guan, Maneesh Agrawala, Hongbo Fu, Alan Zhao, Anyi Rao View a PDF of the paper titled EvalVerse: Pipeline-Aware and Expert-Calibrated Benchmarking for Professional Cinematic Video Generation, by Songlin Yang and 25 other authors View PDF HTML (experimental) Abstract:The rapid evolution of generative video…
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