Pain assessment is a hard subjective problem, but still, it is critical in many medical situations. Many computational approaches explore pain detection and estimation using different types of data and descriptors. Among these, spontaneous facial expressions coded by the Facial Action Coding System (FACS) have achieved outstanding results in frame-by-frame stationary analysis, but not in temporal analysis. We explore spatiotemporal features extracted from video sequences considering pain stimuli as references in the temporal analysis. Our proposal focuses on guided learning by warping the appearance surround the facial action units (AUs). The facial features from frames are processed sequentially to extract their temporal correspondences. These sequences are generated from the original videos and must represent a single-stimulus effect in a short period, so we develop generation policies. Experimental results on the publicly available UNBC-McMaster database have demonstrated that our approach yields significant advances over the state-of-the-art.