The main contributions of the method are three folds. First, we are the first that propose to predict 1D heatmaps on the 𝑥 and 𝑦 axes instead of using 2D heatmaps to locate landmarks and successfully alleviate the quantization error with a fully boosted output resolution. Second, we propose a co-attention module to capture the joint coordinate distribution on the two axes. Third, based on the proposed heatmap regression method, we design a facial landmark detector and tracker which achieve state-of-the-art performance.
