Generative Point Tracking with Flow Matching

• URL: http://arxiv.org/abs/2510.20951v1
• Date: Thu, 23 Oct 2025 19:25:14 GMT
• Title: Generative Point Tracking with Flow Matching
• Authors: Mattie Tesfaldet, Adam W. Harley, Konstantinos G. Derpanis, Derek Nowrouzezahrai, Christopher Pal,
• Abstract summary: We introduce Generative Point Tracker (GenPT), a generative framework for modelling multi-modal trajectories.<n>GenPT is trained with a novel flow matching formulation that combines the iterative refinement of discriminative trackers.<n>We show how our model's generative capabilities can be leveraged to improve point trajectory estimates.
• Score: 32.15342097497571
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tracking a point through a video can be a challenging task due to uncertainty arising from visual obfuscations, such as appearance changes and occlusions. Although current state-of-the-art discriminative models excel in regressing long-term point trajectory estimates -- even through occlusions -- they are limited to regressing to a mean (or mode) in the presence of uncertainty, and fail to capture multi-modality. To overcome this limitation, we introduce Generative Point Tracker (GenPT), a generative framework for modelling multi-modal trajectories. GenPT is trained with a novel flow matching formulation that combines the iterative refinement of discriminative trackers, a window-dependent prior for cross-window consistency, and a variance schedule tuned specifically for point coordinates. We show how our model's generative capabilities can be leveraged to improve point trajectory estimates by utilizing a best-first search strategy on generated samples during inference, guided by the model's own confidence of its predictions. Empirically, we evaluate GenPT against the current state of the art on the standard PointOdyssey, Dynamic Replica, and TAP-Vid benchmarks. Further, we introduce a TAP-Vid variant with additional occlusions to assess occluded point tracking performance and highlight our model's ability to capture multi-modality. GenPT is capable of capturing the multi-modality in point trajectories, which translates to state-of-the-art tracking accuracy on occluded points, while maintaining competitive tracking accuracy on visible points compared to extant discriminative point trackers.

Related papers

• GOT-JEPA: Generic Object Tracking with Model Adaptation and Occlusion Handling using Joint-Embedding Predictive Architecture [27.70912792107499]
  We propose GOT-JEPA, a model-predictive pretraining framework that extends JEPA from predicting image features to predicting tracking models.<n>We further propose Occur to enhance occlusion perception for object tracking.
  arXiv  Detail & Related papers  (2026-02-16T14:26:07Z)
• CoWTracker: Tracking by Warping instead of Correlation [53.834673070954494]
  We propose a dense point tracker that eschews cost volumes in favor of warping.<n>Inspired by recent advances in optical flow, our approach iteratively refines track estimates by warping features from the target frame to the query frame based on the current estimate.<n>Our model is simple and achieves state-of-the-art performance on standard dense point tracking benchmarks, including TAP-Vid-DAVIS, TAP-Vid-Kinetics, and Robo-TAP.
  arXiv  Detail & Related papers  (2026-02-04T18:58:59Z)
• Self-Supervised Any-Point Tracking by Contrastive Random Walks [17.50529887238381]
  We train a global matching transformer to find cycle consistent tracks through video via contrastive random walks. Our method achieves strong performance on the TapVid benchmarks, outperforming previous self-supervised tracking methods.
  arXiv  Detail & Related papers  (2024-09-24T17:59:56Z)
• Trajectory Anomaly Detection with Language Models [21.401931052512595]
  This paper presents a novel approach for trajectory anomaly detection using an autoregressive causal-attention model, termed LM-TAD. By treating trajectories as sequences of tokens, our model learns the probability distributions over trajectories, enabling the identification of anomalous locations with high precision. Our experiments demonstrate the effectiveness of LM-TAD on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-09-18T17:33:31Z)
• Robust Visual Tracking via Iterative Gradient Descent and Threshold Selection [4.978166837959101]
  We introduce a novel robust linear regression estimator, which achieves favorable performance when the error vector follows i.i.d Gaussian-Laplacian distribution. In addition, we expend IGDTS to a generative tracker, and apply IGDTS-distance to measure the deviation between the sample and the model. Experimental results on several challenging image sequences show that the proposed tracker outperformance existing trackers.
  arXiv  Detail & Related papers  (2024-06-02T01:51:09Z)
• LEAP-VO: Long-term Effective Any Point Tracking for Visual Odometry [52.131996528655094]
  We present the Long-term Effective Any Point Tracking (LEAP) module. LEAP innovatively combines visual, inter-track, and temporal cues with mindfully selected anchors for dynamic track estimation. Based on these traits, we develop LEAP-VO, a robust visual odometry system adept at handling occlusions and dynamic scenes.
  arXiv  Detail & Related papers  (2024-01-03T18:57:27Z)
• Time-series Generation by Contrastive Imitation [87.51882102248395]
  We study a generative framework that seeks to combine the strengths of both: Motivated by a moment-matching objective to mitigate compounding error, we optimize a local (but forward-looking) transition policy. At inference, the learned policy serves as the generator for iterative sampling, and the learned energy serves as a trajectory-level measure for evaluating sample quality.
  arXiv  Detail & Related papers  (2023-11-02T16:45:25Z)
• Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior [51.60683890503293]
  We propose a theoretical framework for studying behavior cloning of complex expert demonstrations using generative modeling. We show that pure supervised cloning can generate trajectories matching the per-time step distribution of arbitrary expert trajectories.
  arXiv  Detail & Related papers  (2023-07-27T04:27:26Z)
• Continuity-Discrimination Convolutional Neural Network for Visual Object Tracking [150.51667609413312]
  This paper proposes a novel model, named Continuity-Discrimination Convolutional Neural Network (CD-CNN) for visual object tracking. To address this problem, CD-CNN models temporal appearance continuity based on the idea of temporal slowness. In order to alleviate inaccurate target localization and drifting, we propose a novel notion, object-centroid.
  arXiv  Detail & Related papers  (2021-04-18T06:35:03Z)
• ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking [80.02322563402758]
  One of the core components in online multiple object tracking (MOT) frameworks is associating new detections with existing tracklets. We introduce a probabilistic autoregressive generative model to score tracklet proposals by directly measuring the likelihood that a tracklet represents natural motion.
  arXiv  Detail & Related papers  (2020-04-16T06:43:11Z)
• Deep Multi-Shot Network for modelling Appearance Similarity in Multi-Person Tracking applications [0.0]
  This article presents a Deep Multi-Shot neural model for measuring the Degree of Appearance Similarity (MS-DoAS) between person observations. The model has been deliberately trained to be able to manage the presence of previous identity switches and missed observations in the handled tracks. It has demonstrated a high capacity to discern when a new observation corresponds to a certain track, achieving a classification accuracy of 97% in a hard test.
  arXiv  Detail & Related papers  (2020-04-07T16:43:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.