  Facial landmark detection, or face alignment, is a fundamental task that has
been extensively studied. In this paper, we investigate a new perspective of
facial landmark detection and demonstrate it leads to further notable
improvement. Given that any face images can be factored into space of style
that captures lighting, texture and image environment, and a style-invariant
structure space, our key idea is to leverage disentangled style and shape space
of each individual to augment existing structures via style translation. With
these augmented synthetic samples, our semi-supervised model surprisingly
outperforms the fully-supervised one by a large margin. Extensive experiments
verify the effectiveness of our idea with state-of-the-art results on WFLW,
300W, COFW, and AFLW datasets. Our proposed structure is general and could be
assembled into any face alignment frameworks. The code is made publicly
available at https://github.com/thesouthfrog/stylealign.
