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Heatmap regression methods have dominated face alignment area in recent yearswhile they ignore the inherent relation between different landmarks. In thispaper, we propose a Sparse Local Patch Transformer (SLPT) for learning theinherent relation. The SLPT generates the representation of each singlelandmark from a local patch and aggregates them by an adaptive inherentrelation based on the attention mechanism. The subpixel coordinate of eachlandmark is predicted independently based on the aggregated feature. Moreover,a coarse-to-fine framework is further introduced to incorporate with the SLPT,which enables the initial landmarks to gradually converge to the target faciallandmarks using fine-grained features from dynamically resized local patches.Extensive experiments carried out on three popular benchmarks, including WFLW,300W and COFW, demonstrate that the proposed method works at thestate-of-the-art level with much less computational complexity by learning theinherent relation between facial landmarks. The code is available at theproject website.

Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning