Sekai: A Video Dataset towards World Exploration

Video generation techniques have made remarkable progress, promising to be the foundation of interactive world exploration. However, existing video generation datasets are not well-suited for world exploration training as they suffer from some limitations: limited locations, short duration, static scenes, and a lack of annotations about exploration and the world. In this paper, we introduce Sekai (meaning "world" in Japanese), a high-quality first-person view worldwide video dataset with rich annotations for world exploration. It consists of over 5, 000 hours of walking or drone view (FPV and UVA) videos from over 100 countries and regions across 750 cities. We develop an efficient and effective toolbox to collect, pre-process and annotate videos with location, scene, weather, crowd density, captions, and camera trajectories. Comprehensive analyses and experiments demonstrate the dataset’s scale, diversity, annotation quality, and effectiveness for training video generation models. We believe Sekai will benefit the area of video generation and world exploration, and motivate valuable applications.

Authors

• Zhen Li Beijing Institute of Technology
• Chuanhao Li Beijing Institute of Technology Shenzhen MSU-BIT University
• Xiaofeng Mao
• Shaoheng Lin
• Ming Li
• Shitian Zhao
• Zhaopan Xu Harbin Institute of Technology
• Xinyue Li
• Yukang Feng
• Jianwen Sun Central China Normal University
• Zizhen Li
• Fanrui Zhang
• Jiaxin Ai
• Zhixiang Wang The University of Tokyo National Institute of Informatics
• Yuwei Wu Beijing Institute of Technology Shenzhen MSU-BIT University
• Tong He Shanghai Artificial Intelligence Laboratory
• Yunde Jia Shenzhen MSU-BIT University Beijing Institute of Technology
• Kaipeng Zhang Shanghai AI Laboratory

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