ProTracker: Probabilistic Integration for Robust and Accurate Point Tracking

Tingyang Zhang, Chen Wang, Zhiyang Dou, Gao, Jiahui Lei, Baoquan Chen, Lingjie Liu

ProTracker: Probabilistic Integration for Robust and Accurate Point Tracking: 4 upvotes on Hugging Face Daily Papers, #19 of 19 papers on 2025-01-07. Day-by-day upvote history.

In this paper, we propose ProTracker, a novel framework for robust and accurate long-term dense tracking of arbitrary points in videos. The key idea of our method is incorporating probabilistic integration to refine multiple predictions from both optical flow and semantic features for robust short-term and long-term tracking. Specifically, we integrate optical flow estimations in a probabilistic manner, producing smooth and accurate trajectories by maximizing the likelihood of each prediction. To effectively re-localize challenging points that disappear and reappear due to occlusion, we further incorporate long-term feature correspondence into our flow predictions for continuous trajectory generation. Extensive experiments show that ProTracker achieves the state-of-the-art performance among unsupervised and self-supervised approaches, and even outperforms supervised methods on several benchmarks. Our code and model will be publicly available upon publication.

Paper page on Hugging Face · arXiv

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