TAP-Vid: A Benchmark for Tracking Any Point in a Video (2211.03726v2)

Abstract: Generic motion understanding from video involves not only tracking objects, but also perceiving how their surfaces deform and move. This information is useful to make inferences about 3D shape, physical properties and object interactions. While the problem of tracking arbitrary physical points on surfaces over longer video clips has received some attention, no dataset or benchmark for evaluation existed, until now. In this paper, we first formalize the problem, naming it tracking any point (TAP). We introduce a companion benchmark, TAP-Vid, which is composed of both real-world videos with accurate human annotations of point tracks, and synthetic videos with perfect ground-truth point tracks. Central to the construction of our benchmark is a novel semi-automatic crowdsourced pipeline which uses optical flow estimates to compensate for easier, short-term motion like camera shake, allowing annotators to focus on harder sections of video. We validate our pipeline on synthetic data and propose a simple end-to-end point tracking model TAP-Net, showing that it outperforms all prior methods on our benchmark when trained on synthetic data.

• The paper introduces a novel benchmark for tracking any point in videos, capturing complex deformations and occlusions with human-annotated tracks and a semi-automatic pipeline.
• The methodology leverages an innovative annotation process and a cost volume-inspired TAP-Net to efficiently model spatial relations and occlusion states.
• Empirical evaluations show TAP-Net outperforming baselines like RAFT and COTR, demonstrating robust performance in dynamic, real-world scenarios.

Insights into TAP-Vid: A Benchmark for Tracking Any Point in a Video

The challenge of understanding motion within video frames has always been integral to developing robust computer vision systems. Traditional approaches have focused on object tracking with bounding boxes or segments, optical flow between frame pairs, or keypoint matching on specific object categories such as human figures. However, these methods can falter in capturing surface deformations and non-rigid motions, which are essential for a comprehensive understanding of dynamic scenes. The paper "TAP-Vid: A Benchmark for Tracking Any Point in a Video" proposes a new paradigm called Tracking Any Point (TAP), designed to extend the temporal domain of point tracking and account for occlusions, deformation, and non-rigid motions.

Methodological Innovations

The TAP-Vid paper introduces several innovations to the landscape of point tracking in videos:

1. TAP-Vid Benchmark: The authors propose a dataset spanning a diverse collection of real-world and synthetic videos. This dataset is unique because it includes human-annotated tracks of generic points, effectively capturing complex motions and occlusions. The benchmark consists of four components: TAP-Vid-Kinetics, TAP-Vid-DAVIS, TAP-Vid-Kubric, and TAP-Vid-RGB-Stacking, each serving distinct purposes from evaluation to robotic task simulation.
2. Semi-Automatic Annotation Pipeline: One of the significant contributions is the novel semi-automatic pipeline for annotating real-world videos. By leveraging optical flow, the pipeline aids annotators in concentrating on intricate motion patterns, drastically reducing the manual annotation burden without sacrificing precision.
3. TAP-Net: This is an end-to-end point tracking model optimized for performance on the TAP-Vid dataset. TAP-Net derives inspiration from cost volume methods prominent in optical flow estimation. It infers point locations and visibilities through a dedicated network architecture that considers both spatial relationships and occlusion states.

Empirical Results and Comparative Analysis

The paper presents empirical results illustrating the comparative strength of TAP-Net over conventional methods and even concurrent works such as PIPs. When evaluated on the TAP-Vid benchmark, TAP-Net consistently outperforms baselines like RAFT and COTR. PIPs shows competitive results, particularly in scenarios with smooth, continuous motion. However, TAP-Net demonstrates robustness over a broader array of real-world conditions, including occlusions and rapid motion changes.

Implications and Future Directions

The implications of TAP-Vid and TAP-Net stretch into both theoretical and practical domains. Theoretically, they introduce a new manner of considering motion tracking, encouraging longer timespan predictions and occlusion handling. Practically, the benchmark provides a standard for evaluating future models in generic point tracking. Moreover, the successful implementation of TAP-Net paves the way for its application in other computer vision tasks involving dynamic and deformable objects.

Future developments could focus on enhancing the dataset with more challenging scenarios, such as transparent or liquid surfaces, and exploring methods to handle massive-scale annotation more efficiently. Additionally, integrating more sophisticated machine learning models with TAP principles could push the boundaries of motion understanding further, especially concerning real-time applications and robotic interactions.

In conclusion, TAP-Vid addresses a notable gap in video motion understanding and sets a strong foundation for subsequent research in refined and generalized motion tracking methodologies. The benchmark and approach constitute a step forward, inviting adaptations and developments across the field of computer vision.