Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better (2503.19904v1)

Abstract: Temporal consistency is critical in video prediction to ensure that outputs are coherent and free of artifacts. Traditional methods, such as temporal attention and 3D convolution, may struggle with significant object motion and may not capture long-range temporal dependencies in dynamic scenes. To address this gap, we propose the Tracktention Layer, a novel architectural component that explicitly integrates motion information using point tracks, i.e., sequences of corresponding points across frames. By incorporating these motion cues, the Tracktention Layer enhances temporal alignment and effectively handles complex object motions, maintaining consistent feature representations over time. Our approach is computationally efficient and can be seamlessly integrated into existing models, such as Vision Transformers, with minimal modification. It can be used to upgrade image-only models to state-of-the-art video ones, sometimes outperforming models natively designed for video prediction. We demonstrate this on video depth prediction and video colorization, where models augmented with the Tracktention Layer exhibit significantly improved temporal consistency compared to baselines.

• The paper introduces Tracktention, a novel architectural layer that leverages point tracking to explicitly incorporate motion information and improve temporal consistency in video processing tasks.
• The Tracktention Layer integrates motion via three components: Attentional Sampling pools track info, Track Transformer propagates it temporally, and Attentional Splatting transfers it back to features.
• Experiments show that integrating Tracktention significantly enhances performance and temporal consistency in video depth estimation and colorization, outperforming previous methods.

Leveraging Point Tracking in Video Analysis with Tracktention

The paper "Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better" advances video processing by introducing the Tracktention Layer, a novel architectural component designed to improve temporal consistency in video tasks such as depth estimation and colorization. The authors propose utilizing point tracking to explicitly incorporate motion information into the model, thereby addressing the limitations of previous methods like temporal attention and 3D convolution.

Methodology

The Tracktention Layer integrates motion information by using point tracks, which are sequences of corresponding points across frames. This approach ensures temporal alignment and enhances the handling of complex object motions. It can be seamlessly integrated into existing image-based models, effectively transforming them into video models. Tracktention comprises three main components: Attentional Sampling, Track Transformer, and Attentional Splatting.

1. Attentional Sampling: This component pools information from video feature maps into track tokens by attending to spatial locations specified by point tracks.
2. Track Transformer: It propagates information temporally along each track, maintaining spatial consistency throughout the sequence.
3. Attentional Splatting: It transfers the updated track token information back to the video feature map, ensuring the enriched temporal features are integrated into the original spatial context.

Experiments and Results

The experimental results highlight the efficacy of the Tracktention Layer. When applied to a video depth estimation model, it significantly improves temporal consistency and accuracy, outperforming many existing models, including those specifically designed for video processing. The quantitative evaluations demonstrate reductions in depth prediction errors and improvements in temporal stability. The video depth predictor equipped with the Tracktention Layer showed enhanced performance metrics such as AbsRel and $\delta_{1.25}$ compared to state-of-the-art models like DepthCrafter.

In the domain of video colorization, integrating Tracktention into image-based colorization models yields substantial gains in temporal consistency without compromising vibrancy or realism. The improved Color Distribution Consistency (CDC) scores across benchmarks further substantiate its advantage over traditional methods.

Insights and Implications

The paper provides a compelling case for integrating point tracking mechanisms into video analysis architectures. The explicit modeling of object motion allows Tracktention to outperform implicit methods like 3D convolutions and spatial-temporal attention, which often struggle with large displacements and temporal dependencies. Moreover, the lightweight and plug-and-play nature of Tracktention emphasizes practical applicability, allowing existing image-based models to be upgraded into efficient and effective video models without extensive architectural changes.

Future Directions

The use of state-of-the-art point trackers like CoTracker and TAPIR enhances the robustness of the approach. However, as video lengths and complexities increase, further optimization and exploration of track initialization strategies could be beneficial. Future work might focus on reducing the computational overhead associated with tracking and improving the resilience of trackers in the face of occlusions and complex motion patterns. Additionally, exploring the integration of Tracktention with emerging video processing frameworks could expand its applicability to other video tasks.

In summary, the Tracktention Layer is a significant contribution to video processing, offering a novel approach to enhancing temporal consistency and accuracy by leveraging point tracking. This work lays a foundation for further research into intelligent video attention mechanisms, highlighting the importance of explicit motion modeling in video analysis.