  Heatmap regression methods have dominated face alignment area in recent years
while they ignore the inherent relation between different landmarks. In this
paper, we propose a Sparse Local Patch Transformer (SLPT) for learning the
inherent relation. The SLPT generates the representation of each single
landmark from a local patch and aggregates them by an adaptive inherent
relation based on the attention mechanism. The subpixel coordinate of each
landmark 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 facial
landmarks 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 the
state-of-the-art level with much less computational complexity by learning the
inherent relation between facial landmarks. The code is available at the
project website.
