SpatialVID: A Large-Scale Video Dataset with Spatial Annotations
Authors: name: Jiahao Wang; name: Yufeng Yuan; name: Rujie Zheng; name: Youtian Lin; name: Jian Gao; name: Lin-Zhuo Chen; name: Yajie Bao; name: Yi Zhang; name: Chang Zeng; name: Yanxi Zhou; name: Xiaoxiao Long; name: Hao Zhu; name: Zhaoxiang Zhang; name: Xun Cao; name: Yao Yao
Published: 2025-09-11 17:59:31

Abstract: Significant progress has been made in spatial intelligence, spanning both
spatial reconstruction and world exploration. However, the scalability and
real-world fidelity of current models remain severely constrained by the
scarcity of large-scale, high-quality training data. While several datasets
provide camera pose information, they are typically limited in scale,
diversity, and annotation richness, particularly for real-world dynamic scenes
with ground-truth camera motion. To this end, we collect SpatialVID, a
dataset consists of a large corpus of in-the-wild videos with diverse scenes,
camera movements and dense 3D annotations such as per-frame camera poses,
depth, and motion instructions. Specifically, we collect more than 21,000 hours
of raw video, and process them into 2.7 million clips through a hierarchical
filtering pipeline, totaling 7,089 hours of dynamic content. A subsequent
annotation pipeline enriches these clips with detailed spatial and semantic
information, including camera poses, depth maps, dynamic masks, structured
captions, and serialized motion instructions. Analysis of SpatialVID's data
statistics reveals a richness and diversity that directly foster improved model
generalization and performance, establishing it as a key asset for the video
and 3D vision research community.