CoTracker3 is a state-of-the-art point tracking model that achieves impressive performance with significantly less training data compared to previous models. It introduces architectural simplifications, a novel semi-supervised training pipeline, and improved efficiency. By leveraging multiple pre-trained trackers as teachers to generate pseudo-labels for real-world videos, CoTracker3 reduces the dependency on synthetic data and achieves even better accuracy with a thousandfold reduction in real data requirements. The model highlights the power of considering dependencies between tracked points and utilizing context to enhance tracking accuracy.