  Accurate facial landmarks are essential prerequisites for many tasks related
to human faces. In this paper, an accurate facial landmark detector is proposed
based on cascaded transformers. We formulate facial landmark detection as a
coordinate regression task such that the model can be trained end-to-end. With
self-attention in transformers, our model can inherently exploit the structured
relationships between landmarks, which would benefit landmark detection under
challenging conditions such as large pose and occlusion. During cascaded
refinement, our model is able to extract the most relevant image features
around the target landmark for coordinate prediction, based on deformable
attention mechanism, thus bringing more accurate alignment. In addition, we
propose a novel decoder that refines image features and landmark positions
simultaneously. With few parameter increasing, the detection performance
improves further. Our model achieves new state-of-the-art performance on
several standard facial landmark detection benchmarks, and shows good
generalization ability in cross-dataset evaluation.
