Sekai: A Video Dataset towards World Exploration
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915605401042944 |
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| author | Li, Zhen Li, Chuanhao Mao, Xiaofeng Lin, Shaoheng Li, Ming Zhao, Shitian Xu, Zhaopan Li, Xinyue Feng, Yukang Sun, Jianwen Li, Zizhen Zhang, Fanrui Ai, Jiaxin Wang, Zhixiang Wu, Yuwei He, Tong Pang, Jiangmiao Qiao, Yu Jia, Yunde Zhang, Kaipeng |
| author_facet | Li, Zhen Li, Chuanhao Mao, Xiaofeng Lin, Shaoheng Li, Ming Zhao, Shitian Xu, Zhaopan Li, Xinyue Feng, Yukang Sun, Jianwen Li, Zizhen Zhang, Fanrui Ai, Jiaxin Wang, Zhixiang Wu, Yuwei He, Tong Pang, Jiangmiao Qiao, Yu Jia, Yunde Zhang, Kaipeng |
| contents | 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. The project page is https://lixsp11.github.io/sekai-project/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15675 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sekai: A Video Dataset towards World Exploration Li, Zhen Li, Chuanhao Mao, Xiaofeng Lin, Shaoheng Li, Ming Zhao, Shitian Xu, Zhaopan Li, Xinyue Feng, Yukang Sun, Jianwen Li, Zizhen Zhang, Fanrui Ai, Jiaxin Wang, Zhixiang Wu, Yuwei He, Tong Pang, Jiangmiao Qiao, Yu Jia, Yunde Zhang, Kaipeng Computer Vision and Pattern Recognition Artificial Intelligence 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. The project page is https://lixsp11.github.io/sekai-project/. |
| title | Sekai: A Video Dataset towards World Exploration |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2506.15675 |