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Deep learning technique has dramatically boosted the performance of facealignment algorithms. However, due to large variability and lack of samples,the alignment problem in unconstrained situations, \emph{e.g}\onedot large headposes, exaggerated expression, and uneven illumination, is still largelyunsolved. In this paper, we explore the instincts and reasons behind our twoproposals, \emph{i.e}\onedot Propagation Module and Focal Wing Loss, to tacklethe problem. Concretely, we present a novel structure-infused face alignmentalgorithm based on heatmap regression via propagating landmark heatmaps toboundary heatmaps, which provide structure information for further attentionmap generation. Moreover, we propose a Focal Wing Loss for mining andemphasizing the difficult samples under in-the-wild condition. In addition, weadopt methods like CoordConv and Anti-aliased CNN from other fields thataddress the shift-variance problem of CNN for face alignment. When implementingextensive experiments on different benchmarks, \emph{i.e}\onedot WFLW, 300W,and COFW, our method outperforms state-of-the-arts by a significant margin. Ourproposed approach achieves 4.05\% mean error on WFLW, 2.93\% mean error on 300Wfull-set, and 3.71\% mean error on COFW.

PropagationNet: Propagate Points to Curve to Learn Structure Information