  To endow models with greater understanding of physics and motion, it is
useful to enable them to perceive how solid surfaces move and deform in real
scenes. This can be formalized as Tracking-Any-Point (TAP), which requires the
algorithm to track any point on solid surfaces in a video, potentially densely
in space and time. Large-scale groundtruth training data for TAP is only
available in simulation, which currently has a limited variety of objects and
motion. In this work, we demonstrate how large-scale, unlabeled, uncurated
real-world data can improve a TAP model with minimal architectural changes,
using a selfsupervised student-teacher setup. We demonstrate state-of-the-art
performance on the TAP-Vid benchmark surpassing previous results by a wide
margin: for example, TAP-Vid-DAVIS performance improves from 61.3% to 67.4%,
and TAP-Vid-Kinetics from 57.2% to 62.5%. For visualizations, see our project
webpage at https://bootstap.github.io/
