SpatialTrackerV2: 3D Point Tracking Made Easy
Authors: name: Yuxi Xiao; name: Jianyuan Wang; name: Nan Xue; name: Nikita Karaev; name: Yuri Makarov; name: Bingyi Kang; name: Xing Zhu; name: Hujun Bao; name: Yujun Shen; name: Xiaowei Zhou
Published: 2025-07-16 17:59:03

Abstract: 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.