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Facial alignment involves finding a set of landmark points on an image with aknown semantic meaning. However, this semantic meaning of landmark points isoften lost in 2D approaches where landmarks are either moved to visibleboundaries or ignored as the pose of the face changes. In order to extractconsistent alignment points across large poses, the 3D structure of the facemust be considered in the alignment step. However, extracting a 3D structurefrom a single 2D image usually requires alignment in the first place. Wepresent our novel approach to simultaneously extract the 3D shape of the faceand the semantically consistent 2D alignment through a 3D Spatial TransformerNetwork (3DSTN) to model both the camera projection matrix and the warpingparameters of a 3D model. By utilizing a generic 3D model and a Thin PlateSpline (TPS) warping function, we are able to generate subject specific 3Dshapes without the need for a large 3D shape basis. In addition, our proposednetwork can be trained in an end-to-end framework on entirely synthetic datafrom the 300W-LP dataset. Unlike other 3D methods, our approach only requiresone pass through the network resulting in a faster than real-time alignment.Evaluations of our model on the Annotated Facial Landmarks in the Wild (AFLW)and AFLW2000-3D datasets show our method achieves state-of-the-art performanceover other 3D approaches to alignment.

Faster Than Real-time Facial Alignment: A 3D Spatial Transformer Network Approach in Unconstrained Poses