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.

本文介绍了Sparse Local Patch Transformer（SLPT）用于学习人脸对齐中的内在关系。该模型通过注意机制从局部补丁中生成每个单独地标的表示，并通过自适应内在关系聚合它们。每个地标的子像素坐标根据聚合特征独立预测，并进一步引入粗到细的框架来与SLPT相结合，通过动态调整大小的局部补丁的精细特征，使初始地标逐渐收敛于目标人脸地标。在WFLW、300W和COFW三个流行基准测试中，该方法得到了实验验证，表明通过学习面部标志之间的内在关系，提出的方法在计算复杂性上工作在最先进的水平。

用于鲁棒人脸对齐和特征关键点学习的稀疏局部 Patch Transformer