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

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.