Most state-of-the-art point trackers are trained on synthetic data due to the difficulty of
annotating real videos for this task. However, this can result in suboptimal performance due …

In this paper, we present TAPTRv2, a Transformer-based approach built upon TAPTR for
solving the Tracking Any Point (TAP) task. TAPTR borrows designs from DEtection …

This work aims to address an advanced keypoint detection problem: how to accurately
detect any keypoints in complex real-world scenarios, which involves massive, messy, and …

While recent foundational video generators produce visually rich output, they still struggle
with appearance drift, where objects gradually degrade or change inconsistently across …

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 …

Tracking dense 3D motion from monocular videos remains challenging, particularly when
aiming for pixel-level precision over long sequences. We introduce DELTA, a novel method …

In this paper, we consider the problem of long-term point tracking, which requires consistent
identification of points across multiple frames in a video, despite changes in appearance …

In this paper, we present TAPTRv3, which is built upon TAPTRv2 to improve its point
tracking robustness in long videos. TAPTRv2 is a simple DETR-like framework that can …

In this work, we present MFTIQ, a novel dense long-term tracking model that advances the
Multi-Flow Tracker (MFT) framework to address challenges in point-level visual tracking in …

Point tracking in videos is a fundamental task with applications in robotics, video editing, and
more. While many vision tasks benefit from pre-trained feature backbones to improve …