CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos

Abstract: 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 to the statistical gap between synthetic and real videos. In order to understand these issues better, we introduce CoTracker3, comprising a new tracking model and a new semi-supervised training recipe. This allows real videos without annotations to be used during training by generating pseudo-labels using off-the-shelf teachers. The new model eliminates or simplifies components from previous trackers, resulting in a simpler and often smaller architecture. This training scheme is much simpler than prior work and achieves better results using 1,000 times less data. We further study the scaling behaviour to understand the impact of using more real unsupervised data in point tracking. The model is available in online and offline variants and reliably tracks visible and occluded points.

• The paper introduces a simplified point tracking model that leverages pseudo-labelling of real videos to bridge the synthetic-real gap.
• It achieves state-of-the-art performance with only 0.1% of the training data used by previous methods, emphasizing data efficiency.
• The transformer-based design with cross-track attention robustly handles occlusions and ambiguous points in dynamic video sequences.

An Analysis of CoTracker3: Advancements in Point Tracking via Pseudo-Labelling

The paper "CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos" introduces CoTracker3, an innovative architecture for point tracking in video sequences. CoTracker3 is noted for its simplicity and efficiency, leveraging a semi-supervised pseudo-labelling technique that sets it apart from previous models in this field.

Key Contributions

The primary contributions of CoTracker3 include:

1. Simplified Architecture: CoTracker3 incorporates elements from existing models, such as the iterative updates and correlation volumes of PIPs and LocoTrack, but eliminates unnecessary components to form a more streamlined architecture. This results in a model that is not only smaller and faster but also more capable of handling occlusions.
2. Pseudo-Labelling with Real Videos: CoTracker3 uses a semi-supervised approach by generating pseudo-labels from large collections of real videos. Previous models relied heavily on synthetic datasets, which often introduced a distribution gap when applied to real-world scenarios. By integrating real video data, CoTracker3 bridges this gap, thereby enhancing performance.
3. Data Efficiency: Remarkably, CoTracker3 achieves state-of-the-art performance using only 0.1% of the training data utilized by previous methods like BootsTAPIR. This efficiency is critical when considering the computational and resource constraints in large-scale training environments.
4. Joint and Robust Tracking: Through its transformer-based design, CoTracker3 employs cross-track attention mechanisms that improve the tracking of occluded and ambiguous points by leveraging correlations between multiple points in the video.

Experimental Evaluation

The authors subjected CoTracker3 to rigorous benchmarks, including TAP-Vid and Dynamic Replica, demonstrating its superiority in both visible and occluded point tracking. Specifically, CoTracker3 achieved strong numerical results across a variety of metrics and datasets, including Kinetics, RGB-Stacking, and DAVIS, outperforming recent models such as TAPIR and LocoTrack.

Theoretical and Practical Implications

The research presents several implications:

• Practical Impact: By reducing reliance on large synthetic datasets, CoTracker3 simplifies the deployment and training processes, making it accessible for a broader range of applications, such as 3D reconstruction and dynamic scene understanding. The model's adaptability to real-world video datasets enhances its applicability in diverse settings.
• Theoretical Insights: CoTracker3 challenges the necessity of complex training protocols by demonstrating that a simpler, more efficient approach can yield superior results. This raises interesting questions about the fundamental requirements for effective point tracking and may inspire a reevaluation of architectural and training complexities in similar tasks.

Future Directions

Looking forward, CoTracker3 opens several avenues for further exploration:

• Teacher Model Diversity: Enhancing the diversity and efficacy of teacher models could further improve the student model's performance through more robust pseudo-labelling techniques.
• Iterative Scaling Techniques: Investigating the limits of iterative scaling and developing more effective strategies for continuous improvement could yield additional enhancements in model accuracy and robustness.
• Integrating Other Video Data Sources: Expanding the range of video data sources to include more challenging or niche environments could refine the model's adaptability and generalization capabilities.

In conclusion, CoTracker3 represents a significant step forward in point tracking by marrying simplicity with advanced pseudo-labelling techniques. Its ability to achieve superior results with minimal data highlights the potential for efficient and scalable solutions in computer vision tasks. This paper not only advances the state-of-the-art but also provides a robust framework for future research and applications in the domain.