TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement (2306.08637v2)

Abstract: We present a novel model for Tracking Any Point (TAP) that effectively tracks any queried point on any physical surface throughout a video sequence. Our approach employs two stages: (1) a matching stage, which independently locates a suitable candidate point match for the query point on every other frame, and (2) a refinement stage, which updates both the trajectory and query features based on local correlations. The resulting model surpasses all baseline methods by a significant margin on the TAP-Vid benchmark, as demonstrated by an approximate 20% absolute average Jaccard (AJ) improvement on DAVIS. Our model facilitates fast inference on long and high-resolution video sequences. On a modern GPU, our implementation has the capacity to track points faster than real-time, and can be flexibly extended to higher-resolution videos. Given the high-quality trajectories extracted from a large dataset, we demonstrate a proof-of-concept diffusion model which generates trajectories from static images, enabling plausible animations. Visualizations, source code, and pretrained models can be found on our project webpage.

• The paper introduces a dual-stage model that fuses per-frame initialization with temporal refinement to achieve a 20% AJ improvement on the TAP-Vid benchmark.
• The methodology employs convolutional architectures for efficient real-time processing, robust occlusion recovery, and reliable uncertainty estimation.
• Key innovations include integrating TAP-Net and PIPs to combine global search with local trajectory smoothing, paving the way for applications in animation, robotics, and AR.

Overview of TAPIR: Tracking Any Point with Per-frame Initialization and Temporal Refinement

The paper "TAPIR: Tracking Any Point with Per-frame Initialization and Temporal Refinement" presents an innovative model designed to track any queried point on any physical surface across a video sequence. The authors introduce a two-stage model, TAPIR, which significantly advances the state of the art in the field of point-level correspondence in computer vision. Unlike other prevalent methodologies, TAPIR exhibits robust performance in tracking, occlusion recovery, and is optimized for real-time applications.

The core contribution of the paper is the integration of two distinct components: per-frame initialization and temporal refinement. The initial phase involves identifying candidate point matches for the query point across independent video frames, while the refinement phase adjusts the trajectory and updates query features using local correlations. This dual-stage approach allows TAPIR to outperform baseline methods significantly, achieving approximately a 20% absolute average Jaccard (AJ) improvement on the TAP-Vid benchmark, particularly on the DAVIS dataset.

The paper also highlights computational efficiency as a salient feature of the TAPIR model. On contemporary GPU setups, TAPIR performs faster than real-time, making it feasible for deployment in scenarios involving extensive high-resolution video datasets. Furthermore, TAPIR extends its functionality to generate trajectories from static images, offering potential applications in realistic animation generation from single images through a proof-of-concept diffusion model.

TAPIR represents a leap forward in the domain of video tracking, primarily by enhancing existing architectures like TAP-Net and Persistent Independent Particles (PIPs). The model combines the global search capability of TAP-Net with the trajectory smoothing advantage of PIPs, while introducing convolutional layers to efficiently handle temporal refinement.

Key Architectural Decisions

1. Coarse-to-Fine Approach: The TAPIR model employs a hierarchical method starting with a coarse initialization that identifies potential matches and follows up with detailed refinement using higher resolution data.
2. Fully-Convolutional in Time: Leveraging temporal and spatial convolutions, TAPIR achieves efficient computation on GPU and TPU infrastructures. This design choice ensures scalability and speed.
3. Uncertainty Estimation: Crucially, TAPIR estimates its own predictive uncertainty, enabling the suppression of low-confidence predictions. This mechanism is pivotal for improving the reliability and accuracy of the trajectories.
4. Synchronization of TAP-Net and PIPs: The amalgamation of TAP-Net’s robust frame-matching and PIPs’s refinement techniques addresses the weaknesses of each when applied standalone, thus presenting a holistic tracking solution.

Numerical Achievements and Implications

The empirical evaluations demonstrate TAPIR's prowess with extensive performance improvements on the TAP-Vid benchmark datasets. Compared to prior state-of-the-art models, TAPIR enhances DAVIS results by 20%, showcasing its refined tracking accuracy and managing complex video elements such as occlusions and motion blur.

Beyond the immediate impact on video tracking technology, TAPIR's architectural insights hold theoretical implications for AI and ML frameworks. The self-supervised refinement and novel approach to hierarchical feature aggregation set a precedent for future models in this area. TAPIR's practical applications can extend to robotics, augmented reality, and content creation, where nuanced motion tracking is paramount.

Future Prospects

The paper opens potential avenues for further exploration, such as improving model resilience to dynamic scene changes and optimizing training processes for diverse datasets. Future research may also focus on enhancing TAPIR's model architecture to handle environments with minimal texture and increasing its adaptability to unseen contexts or camera movements.

In summary, TAPIR's contribution lies in its methodological rigor and innovative design, substantially advancing the field of point tracking in videos. The findings and methodologies described in this paper lay a robust foundation for future advancements in visual correspondence and beyond.