Real-World Point Tracking with Verifier-Guided Pseudo-Labeling
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914388042055680 |
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| author | Aydemir, Görkay Güney, Fatma Xie, Weidi |
| author_facet | Aydemir, Görkay Güney, Fatma Xie, Weidi |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12217 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Real-World Point Tracking with Verifier-Guided Pseudo-Labeling Aydemir, Görkay Güney, Fatma Xie, Weidi Computer Vision and Pattern Recognition 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 |
| title | Real-World Point Tracking with Verifier-Guided Pseudo-Labeling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.12217 |