详细信息
- 来源站点
- ArXiv CS.CV
- 作者
- Diandian Zhang, Tingyu Song, Lin Fu, Zheyuan Yang, Yilun Zhao
- 文章类型
- PAPER
- 语言
- en
- 发布日期
- 2026-08-11
摘要
arXiv:2608.09873v1 Announce Type: new Abstract: We introduce Sci-VBench, a comprehensive benchmark for evaluating knowledge- and reasoning-intensive video generation across scientific domains. It contains 1,253 expert-annotated examples spanning 60 subjects across four core disciplines: Natural Science, Healthcare, Humanities & Social Sciences, and Engineering. Each example requires models to generate temporally rich videos that demand scientific reasoning and knowledge-grounded synthesis, going beyond surface-level visual plausibility. We further establish a rubric-based evaluation protocol. Our analysis shows that, under this protocol, both non-expert human evaluators and MLLM-as-Judge systems can achieve relatively high agreement with expert judgments, supporting reproducible evaluation at scale.