Face alignment refers to the process of estimating the position of a number of salient landmarks on face images or videos, such as mouth and eye corners, nose tip, etc. With the availability of large annotated databases and the rise of deep learning-based methods, face alignment as a domain has matured to a point where it can be applied in more or less unconstrained conditions, e.g. non-frontal head poses, presence of heavy make-up or partial occlusions. However, when considering real-case alignment on videos with possibly low frame rates, we need to make sure that the algorithms are robust to jittering of the face bounding box localization, lowresolution of the face crops, possible bad environmental lighting, brightness, and presence of noise. To tackle these issues, we propose RULe, a three-staged Relocalization-Uniformization-Landmark Estimation network. In the first stage, an initial loosely localized bounding box gets refined to output a well centered face crop, thus reducing the variability of the images prior to passing them to the subsequent stage. Then, in the second stage, the face style is uniformized (using adversarial learning as well as perceptual losses) to correct low resolution or variations of brightness/contrast. Finally, the third stage outputs a precise landmark estimation given such enhanced face crop using a cascaded compact model trained using hint-based knowledge distillation. We show through a variety of experiments that RULe achieves realtime face alignment with state-of-the-art precision in heavily degraded conditions.