  This paper presents a Refinement Pyramid Transformer (RePFormer) for robust
facial landmark detection. Most facial landmark detectors focus on learning
representative image features. However, these CNN-based feature representations
are not robust enough to handle complex real-world scenarios due to ignoring
the internal structure of landmarks, as well as the relations between landmarks
and context. In this work, we formulate the facial landmark detection task as
refining landmark queries along pyramid memories. Specifically, a pyramid
transformer head (PTH) is introduced to build both homologous relations among
landmarks and heterologous relations between landmarks and cross-scale
contexts. Besides, a dynamic landmark refinement (DLR) module is designed to
decompose the landmark regression into an end-to-end refinement procedure,
where the dynamically aggregated queries are transformed to residual
coordinates predictions. Extensive experimental results on four facial landmark
detection benchmarks and their various subsets demonstrate the superior
performance and high robustness of our framework.
