We tackle the problem of Tracking Any Point (TAP) in videos, which
specifically aims at estimating persistent long-term trajectories of query
points in videos. Previous methods attempted to estimate these trajectories
independently to incorporate longer image sequences, therefore, ignoring the
potential benefits of incorporating spatial context features. We argue that
independent video point tracking also demands spatial context features. To this
end, we propose a novel framework Context-TAP, which effectively improves point
trajectory accuracy by aggregating spatial context features in videos.
Context-TAP contains two main modules: 1) a SOurse Feature Enhancement (SOFE)
module, and 2) a TArget Feature Aggregation (TAFA) module. Context-TAP
significantly improves PIPs all-sided, reducing 11.4% Average Trajectory Error
of Occluded Points (ATE-Occ) on CroHD and increasing 11.8% Average Percentage
of Correct Keypoint (A-PCK) on TAP-Vid-Kinectics. Demos are available at this
$\href{this https URL}{webpage}$.

该研究提出了一种新的视频点追踪框架 Context-TAP，通过聚合空间上下文特征，有效地提高了点轨迹的准确性。