MEV：面向长镜头多事件视频生成的数据集
MEV: A Multi-Event Video Dataset for Long-Take Generation
做视频生成的朋友可以看看这个 MEV 数据集，专门针对一个长镜头里连续多个事件的场景，带时间边界标注，比常见的单事件短片数据集更对口。
研究团队发布 MEV 数据集，专门用于多事件长镜头（long-take）视频生成。数据集由真实世界视频构成，主体在单个镜头中按顺序完成多个互不重叠的事件，带有精确的时间边界标注和细粒度程序性描述。配套的标注管线可将视频切分为时间对齐的事件单元，并生成全局级与事件级两层结构化描述。在事件中心的评测协议下，用 MEV 微调并施加时间对齐的事件条件后，模型在多事件生成实验中表现出更好的长镜头事件转场和更连贯的内容。
MEV: A Multi-Event Video Dataset for Long-Take Generation
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.