SpatialTrackerV2: 3D Point Tracking Made Easy
We present SpatialTrackerV2, a feed-forward 3D point tracking method for
monocular videos. Going beyond modular pipelines built on off-the-shelf
components for 3D tracking, our approach unifies the intrinsic connections
between point tracking, monocular depth, and camera pose estimation into a
high-performing and feedforward 3D point tracker. It decomposes world-space 3D
motion into scene geometry, camera ego-motion, and pixel-wise object motion,
with a fully differentiable and end-to-end architecture, allowing scalable
training across a wide range of datasets, including synthetic sequences, posed
RGB-D videos, and unlabeled in-the-wild footage. By learning geometry and
motion jointly from such heterogeneous data, SpatialTrackerV2 outperforms
existing 3D tracking methods by 30%, and matches the accuracy of leading
dynamic 3D reconstruction approaches while running 50times faster.
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Yuxi Xiao
claimed_verified
2025-07-18T07:50:34.312Z
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Yuxi Xiao
user
Yuxihenry
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Jianyuan Wang
admin_assigned
2025-07-17T09:37:32.434Z
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Jianyuan Wang
user
JianyuanWang
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Nan Xue
claimed_verified
2025-07-17T08:15:41.525Z
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Nan
user
cherubicxn
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Nikita Karaev
admin_assigned
2025-07-17T09:40:30.784Z
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Nikita Karaev
user
nikkar
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Yuri Makarov
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Bingyi Kang
admin_assigned
2025-07-17T09:40:39.614Z
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Bingyi Kang
user
bykang
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Xing Zhu
68785eb6001546c83aa4f962
Hujun Bao
68785eb6001546c83aa4f963
Yujun Shen
68785eb6001546c83aa4f964
Xiaowei Zhou
SpatialTrackerV2: 3D Point Tracking Made Easy
We present SpatialTrackerV2, a feed-forward 3D point tracking method for
monocular videos. Going beyond modular pipelines built on off-the-shelf
components for 3D tracking, our approach unifies the intrinsic connections
between point tracking, monocular depth, and camera pose estimation into a
high-performing and feedforward 3D point tracker. It decomposes world-space 3D
motion into scene geometry, camera ego-motion, and pixel-wise object motion,
with a fully differentiable and end-to-end architecture, allowing scalable
training across a wide range of datasets, including synthetic sequences, posed
RGB-D videos, and unlabeled in-the-wild footage. By learning geometry and
motion jointly from such heterogeneous data, SpatialTrackerV2 outperforms
existing 3D tracking methods by 30%, and matches the accuracy of leading
dynamic 3D reconstruction approaches while running 50times faster.
SpatialTrackerV2 is a feed-forward 3D point tracking method for monocular videos that integrates scene geometry, camera ego-motion, and object motion into a unified, differentiable architecture, achieving high accuracy and speed.
feed-forward 3D point tracking monocular videos scene geometry camera ego-motion pixel-wise object motion fully differentiable end-to-end architecture synthetic sequences posed RGB-D videos unlabeled in-the-wild footage dynamic 3D reconstruction