SpatialTracker: Tracking Any 2D Pixels in 3D Space
Recovering dense and long-range pixel motion in videos is a challenging
problem. Part of the difficulty arises from the 3D-to-2D projection process,
leading to occlusions and discontinuities in the 2D motion domain. While 2D
motion can be intricate, we posit that the underlying 3D motion can often be
simple and low-dimensional. In this work, we propose to estimate point
trajectories in 3D space to mitigate the issues caused by image projection. Our
method, named SpatialTracker, lifts 2D pixels to 3D using monocular depth
estimators, represents the 3D content of each frame efficiently using a
triplane representation, and performs iterative updates using a transformer to
estimate 3D trajectories. Tracking in 3D allows us to leverage
as-rigid-as-possible (ARAP) constraints while simultaneously learning a
rigidity embedding that clusters pixels into different rigid parts. Extensive
evaluation shows that our approach achieves state-of-the-art tracking
performance both qualitatively and quantitatively, particularly in challenging
scenarios such as out-of-plane rotation.
6614cb62a025df49c6a03b6f
Yuxi Xiao
claimed_verified
2025-07-17T08:16:24.852Z
6688a8f30bf195d6e53ac28d
https://cdn-avatars.huggingface.co/v1/production/uploads/6688a8f30bf195d6e53ac28d/5izbVmdCWWwA1wBjcxZPB.jpeg
Yuxi Xiao
user
Yuxihenry
6614cb62a025df49c6a03b70
Qianqian Wang
6614cb62a025df49c6a03b71
Shangzhan Zhang
6614cb62a025df49c6a03b72
Nan Xue
claimed_verified
2025-07-17T08:16:18.048Z
6485ce5ec7f19728a49df17a
/avatars/e83966e6906c1d0f151300981e30f85a.svg
Nan
user
cherubicxn
6614cb62a025df49c6a03b73
Sida Peng
6614cb62a025df49c6a03b74
Yujun Shen
6614cb62a025df49c6a03b75
Xiaowei Zhou
SpatialTracker: Tracking Any 2D Pixels in 3D Space
Recovering dense and long-range pixel motion in videos is a challenging
problem. Part of the difficulty arises from the 3D-to-2D projection process,
leading to occlusions and discontinuities in the 2D motion domain. While 2D
motion can be intricate, we posit that the underlying 3D motion can often be
simple and low-dimensional. In this work, we propose to estimate point
trajectories in 3D space to mitigate the issues caused by image projection. Our
method, named SpatialTracker, lifts 2D pixels to 3D using monocular depth
estimators, represents the 3D content of each frame efficiently using a
triplane representation, and performs iterative updates using a transformer to
estimate 3D trajectories. Tracking in 3D allows us to leverage
as-rigid-as-possible (ARAP) constraints while simultaneously learning a
rigidity embedding that clusters pixels into different rigid parts. Extensive
evaluation shows that our approach achieves state-of-the-art tracking
performance both qualitatively and quantitatively, particularly in challenging
scenarios such as out-of-plane rotation.
SpatialTracker estimates 3D point trajectories using monocular depth estimation and transformer updates, achieving superior video tracking performance with ARAP constraints and rigidity embeddings.
monocular depth estimators triplane representation transformer as-rigid-as-possible (ARAP) constraints rigidity embedding