Sekai: A Video Dataset towards World Exploration

TL;DR

Sekai is a large, diverse, and richly annotated first-person video dataset from around the world, designed to advance video generation and world exploration research.

Contribution

The paper introduces Sekai, a comprehensive worldwide video dataset with extensive annotations, addressing limitations of existing datasets for world exploration training.

Findings

Demonstrates the dataset's scale and diversity.

Shows effectiveness in training video generation models.

Provides high-quality annotations for various exploration aspects.

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. The project page is https://lixsp11.github.io/sekai-project/.