Temporal consistency is critical in video prediction. Traditional methods, such as temporal attention mechanisms and 3D convolutions, often struggle with significant object movements and fail to capture long-range temporal dependencies in dynamic scenes. To address these limitations, we propose the Tracktention Layer, a novel architectural component that explicitly integrates motion information using point tracks — 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. Empirical evaluations on standard video estimation benchmarks demonstrate that models augmented with the Tracktention Layer exhibit significantly improved temporal consistency compared to baseline models.