Real-World Point Tracking with Verifier-Guided Pseudo-Labeling

• URL: http://arxiv.org/abs/2603.12217v1
• Date: Thu, 12 Mar 2026 17:40:52 GMT
• Title: Real-World Point Tracking with Verifier-Guided Pseudo-Labeling
• Abstract summary: We introduce verifier, a meta-model that learns to assess the reliability of tracker predictions and guide pseudo-label generation.<n>Our approach achieves state-of-the-art results while requiring less data than prior self-training methods.
• Score: 57.82074913456958
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Models for long-term point tracking are typically trained on large synthetic datasets. The performance of these models degrades in real-world videos due to different characteristics and the absence of dense ground-truth annotations. Self-training on unlabeled videos has been explored as a practical solution, but the quality of pseudo-labels strongly depends on the reliability of teacher models, which vary across frames and scenes. In this paper, we address the problem of real-world fine-tuning and introduce verifier, a meta-model that learns to assess the reliability of tracker predictions and guide pseudo-label generation. Given candidate trajectories from multiple pretrained trackers, the verifier evaluates them per frame and selects the most trustworthy predictions, resulting in high-quality pseudo-label trajectories. When applied for fine-tuning, verifier-guided pseudo-labeling substantially improves the quality of supervision and enables data-efficient adaptation to unlabeled videos. Extensive experiments on four real-world benchmarks demonstrate that our approach achieves state-of-the-art results while requiring less data than prior self-training methods. Project page: https://kuis-ai.github.io/track_on_r

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