Sekai: A Video Dataset Towards World Exploration
Abstract
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.
Cite
Text
Li et al. "Sekai: A Video Dataset Towards World Exploration." Advances in Neural Information Processing Systems, 2025.Markdown
[Li et al. "Sekai: A Video Dataset Towards World Exploration." Advances in Neural Information Processing Systems, 2025.](https://mlanthology.org/neurips/2025/li2025neurips-sekai/)BibTeX
@inproceedings{li2025neurips-sekai,
title = {{Sekai: A Video Dataset Towards World Exploration}},
author = {Li, Zhen and Li, Chuanhao and Mao, Xiaofeng and Lin, Shaoheng and Li, Ming and Zhao, Shitian and Pan, xu Zhao and Li, Xinyue and Feng, Yukang and Sun, Jianwen and Li, Zizhen and Zhang, Fanrui and Ai, Jiaxin and Wang, Zhixiang and Wu, Yuwei and He, Tong and Jia, Yunde and Zhang, Kaipeng},
booktitle = {Advances in Neural Information Processing Systems},
year = {2025},
url = {https://mlanthology.org/neurips/2025/li2025neurips-sekai/}
}