AllTracker: Efficient Dense Point Tracking at High Resolution (2506.07310v1)

Abstract: We introduce AllTracker: a model that estimates long-range point tracks by way of estimating the flow field between a query frame and every other frame of a video. Unlike existing point tracking methods, our approach delivers high-resolution and dense (all-pixel) correspondence fields, which can be visualized as flow maps. Unlike existing optical flow methods, our approach corresponds one frame to hundreds of subsequent frames, rather than just the next frame. We develop a new architecture for this task, blending techniques from existing work in optical flow and point tracking: the model performs iterative inference on low-resolution grids of correspondence estimates, propagating information spatially via 2D convolution layers, and propagating information temporally via pixel-aligned attention layers. The model is fast and parameter-efficient (16 million parameters), and delivers state-of-the-art point tracking accuracy at high resolution (i.e., tracking 768x1024 pixels, on a 40G GPU). A benefit of our design is that we can train on a wider set of datasets, and we find that doing so is crucial for top performance. We provide an extensive ablation study on our architecture details and training recipe, making it clear which details matter most. Our code and model weights are available at https://alltracker.github.io .

• The paper introduces AllTracker, a model that generates high-resolution dense point correspondences over hundreds of frames using global correlations.
• The innovative architecture combines 2D convolutions with pixel-aligned attention to efficiently propagate information and achieve state-of-the-art tracking accuracy.
• With only 16 million parameters and extensive ablation studies, the method enhances tracking robustness for applications like autonomous navigation, surveillance, and AR.

AllTracker: Efficient Dense Point Tracking at High Resolution

The paper "AllTracker: Efficient Dense Point Tracking at High Resolution" introduces a model that effectively addresses the challenges associated with dense point tracking in high-resolution video frames. The objective is to estimate long-range point tracks through global correlations across numerous frames, rather than the conventional frame-to-frame optical flow, which inherently limits temporal scope.

Key Contributions

1. Dense High-Resolution Point Tracking: AllTracker sets itself apart by generating high-resolution, dense correspondence maps for every pixel across hundreds of frames. The model establishes optical flow between a query frame and many subsequent frames, circumventing the constraints seen in previous optical flow methods focused solely on adjacent frame correlations.
2. Innovative Architecture: The architecture blends optical flow and point tracking methodologies. AllTracker uses a novel architecture that iteratively refines correspondence estimates. It utilizes 2D convolutions for spatial propagation and pixel-aligned attention layers for temporal propagation, which allows information to be shared across a wider time frame efficiently.
3. Parameter Efficiency: With only 16 million parameters, AllTracker is computationally efficient, providing state-of-the-art tracking accuracy even at resolutions of $768 \times 1024$ pixels on a 40G GPU. This efficiency emerges from a design that works predominantly on low-resolution grids before the final precise upscaling.
4. Extensive Dataset Utilization: AllTracker's design facilitates training across diverse datasets, leveraging both optical flow and point tracking datasets. The paper underscores that a comprehensive mix of training data is pivotal for optimal performance, highlighting the significance of dataset diversity for robust track estimation.
5. Comprehensive Ablation Studies: The research incorporates meticulous ablation studies that dissect architecture details and training procedures, transparently outlining the critical components that enhance model performance.

Numerical Results and Performance

AllTracker exhibits extraordinary performance in point tracking metrics. The model's ability to maintain high fidelity tracking for all pixels and its aptness to work with extended sequences afford it a competitive edge over traditional sparse trackers and flow methods. Through ablation, it is demonstrated that combining temporal priors with spatial awareness contributes significantly to reducing drift and improving robustness against occlusions.

Theoretical and Practical Implications

The development of AllTracker represents a substantial evolution in video sequence analysis and motion estimation. The successful blending of techniques from disparate tracking paradigms in computer vision suggests potential avenues for further innovation in tackling tasks that necessitate long-duration tracking with high spatial resolution.

• Practical Applications: The ability to monitor dense point trajectories across frames has promising applications in areas such as autonomous navigation, surveillance systems, and augmented reality where accurate scene motion estimation is critical.
• Theoretical Advances: The proposed methodology encourages rethinking how temporal information should be integrated within spatial mechanisms, providing a fresh perspective on enhancing the fidelity of high-resolution motion modeling.

Future Directions

The demonstration of effective dense tracking opens questions regarding the scalability of such models to even larger datasets and broader applications. Future research may explore dynamic architectures that adapt to varying complexities within video frames or integrate world-based priors for even more robust motion prediction. Additionally, leveraging 3D modeling techniques may offer further enhancements in tracking scenarios where depth estimation plays a critical role.

AllTracker's code and model weights are made available to facilitate further exploration and development by the research community, indicating a collaborative openness to extend the impact of these findings across related domains.