Abstract
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 ground-truth 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 self-supervised 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/
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Acknowledgements
We thank Jon Scholz, Stannis Zhou, Mel Vecerik, Yusuf Aytar, Viorica Patraucean, Mehdi Sajjadi, Daniel Zoran, and Nando de Freitas for valuable discussions and support, and David Bridson, Lucas Smaira, and Michael King for help on datasets.
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Doersch, C. et al. (2025). BootsTAP: Bootstrapped Training for Tracking-Any-Point. In: Cho, M., Laptev, I., Tran, D., Yao, A., Zha, H. (eds) Computer Vision – ACCV 2024. ACCV 2024. Lecture Notes in Computer Science, vol 15473. Springer, Singapore. https://doi.org/10.1007/978-981-96-0901-7_28
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