Dynamic deformable transformer for end-to-end face alignment

Heatmap-based regression (HBR) methods have dominated for a long time in the face alignment field while these methods need complex design and post-processing. In this study, the authors propose an end-to-end and simple enough coordinate-based regression (CBR) method called Dynamic Deformable Transformer (DDT) for face alignment. Unlike general pre-defined landmark queries, DDT uses Dynamic Landmark Queries (DLQs) to query landmarks' classes and coordinates together. Besides, DDT adopts a deformable attention mechanism rather than a regular attention mechanism which has a faster convergence speed and lower computational complexity. Experiment results on three mainstream datasets 300W, WFLW, and COFW demonstrate DDT exceeds the state-of-the-art CBR methods by a large margin and is comparable to the current state-of-the-art HBR methods with much less computational complexity.