arXiv cs.AI· Peiyuan Zhu, Shaoan Xie, Yifan Shen, Wen Tian, Zijian Li, Guangyi Chen, Kun Zhang·· 14 小时前AI 评分34
MEV：面向长镜头生成的多事件视频数据集
MEV: A Multi-Event Video Dataset for Long-Take Generation
AI 导读
MEV 是一个面向长镜头视频生成的多事件视频数据集，提供主体依次执行多个不重叠事件的高质量真实视频，并带有精确时间边界和细粒度过程标注。配套标注流程将视频切分为时间定位事件，生成全局与事件级结构化描述；在事件中心评测协议下用时间定位事件条件微调后，长镜头事件过渡与内容连贯性均有改善。该数据集已入选 NeurIPS 2026，并公开项目页面。
正文 · 原文
Abstract:Current datasets for text-to-video generation are largely limited to short clips depicting a single event or coarse-grained captions. As a result, they provide limited supervision for modeling long-take temporal dependencies, often leading to temporally inconsistent generations when describing multiple events. In this paper, we introduce MEV, a multi-event video dataset designed for long-take video generation. MEV provides high-quality real-world videos where subjects can perform a sequence of distinct, non-overlapping events, featuring precise temporal boundaries and fine-grained procedural annotations. This event-centric discretization reduces confounding factors and enables models to support temporally localized conditioning and build long-take videos from atomic event units. Furthermore, we introduce a comprehensive annotation pipeline that segments videos into temporally grounded events, and generates structured captions at both the global level and the event level. In addition, under an event-centric evaluation protocol, we conduct multi-event generation experiments by fine-tuning on MEV with temporally grounded event conditioning, leading to improved long-take event transitions and more coherent content. We believe this work provides a publicly available resource for multi-event fine-tuning and event-centric evaluation, together with practical insights into learning from temporally structured video-text data.
| Comments: | Accepted to NeurIPS 2026. Project page: this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.38241 [cs.CV] |
| (or arXiv:2610.38241v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.38241 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Peiyuan Zhu [view email]
[v1]
Wed, 28 Oct 2026 03:43:19 UTC (35,433 KB)
来源：arXiv cs.AI · arxiv.org