SpatialVID: A Large-Scale Video Dataset with Spatial Annotations
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Cited by 33 in World models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
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 videos, 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 fosters improved model generalization and performance, establishing it as a key asset for the video and 3D vision research community.
Key facts. Cited by 77 papers in the map (data through 14 Oct 2026). Builds on 79 of its 90 references that are in the map. First posted on arXiv 11 Sep 2025.
Builds on
79 of 90 references.
References · 79
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50 most cited of 79 All 79 (JSON)
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Citing papers per year: 2025: 11; 2026: 66.
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