Establishing accurate point correspondences in videos is a fundamental challenge in deep learning due to its broad applications across various domains, such as object tracking, action recognition, and scene understanding. Meta AI addresses this challenge with "CoTracker," an innovative architecture for video motion estimation that leverages the transformer network to enhance the prediction of point movements across video frames. CoTracker stands out by utilizing both time and group attention blocks, allowing it to understand motion dynamics and point correlations more effectively than traditional methods. This design improves accuracy, particularly in handling occlusions and complex scene dynamics, while its windowed inference capability allows it to process long videos efficiently. The architecture's innovative approach to point selection and unrolled learning in sliding windows further enhances its adaptability to diverse video lengths and conditions. Through rigorous testing on synthetic and real-world datasets, CoTracker demonstrated superior predictive capabilities, surpassing previous state-of-the-art models like RAFT and PIPs, highlighting its potential as a transformative solution for video motion prediction and point tracking.