SpatialVID: A Large-Scale Video Dataset with Spatial Annotations
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.
68c37eb7fc1747b9124039bd
Jiahao Wang
claimed_verified
2025-09-12T16:08:41.961Z
65f7f415175d2ebe6ebaf236
/avatars/48d0fc5e06df8d1f9d7e1a40a75c0af2.svg
Jeremiah.wang
user
JiaHWang
68c37eb7fc1747b9124039be
Yufeng Yuan
claimed_verified
2025-09-12T16:08:35.317Z
680f2f21a6ecea92b7020173
https://cdn-avatars.huggingface.co/v1/production/uploads/680f2f21a6ecea92b7020173/p1hukdtGohThMPTKJI5ia.jpeg
Felix Yuan
user
FelixYuan
68c37eb7fc1747b9124039bf
Rujie Zheng
claimed_verified
2025-09-12T16:08:39.058Z
67b53c734508bd06175645bf
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/r1SZgTzWFf4XaNQctaTJc.png
Rujie Zheng
user
Rujie030
68c37eb7fc1747b9124039c0
Youtian Lin
claimed_verified
2025-09-13T14:56:23.803Z
645a24779f06c5897254d14b
/avatars/dd0a635674025dcc9a94ee0f4c952083.svg
Youtian Lin
user
LoYoT
68c37eb7fc1747b9124039c1
Jian Gao
68c37eb7fc1747b9124039c2
Lin-Zhuo Chen
68c37eb7fc1747b9124039c3
Yajie Bao
68c37eb7fc1747b9124039c4
Yi Zhang
68c37eb7fc1747b9124039c5
Chang Zeng
68c37eb7fc1747b9124039c6
Yanxi Zhou
68c37eb7fc1747b9124039c7
Xiaoxiao Long
68c37eb7fc1747b9124039c8
Hao Zhu
68c37eb7fc1747b9124039c9
Zhaoxiang Zhang
68c37eb7fc1747b9124039ca
Xun Cao
68c37eb7fc1747b9124039cb
Yao Yao
SpatialVID: A Large-Scale Video Dataset with Spatial Annotations
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.
SpatialVID, a large-scale dataset with diverse videos and dense 3D annotations, enhances model generalization and performance in video and 3D vision research.
spatialVID camera poses depth maps dynamic masks structured captions motion instructions video and 3D vision