  An efficient, fully automatic method for 3D face shape and pose estimation in
unconstrained 2D imagery is presented. The proposed method jointly estimates a
dense set of 3D landmarks and facial geometry using a single pass of a modified
version of the popular "U-Net" neural network architecture. Additionally, we
propose a method for directly estimating a set of 3D Morphable Model (3DMM)
parameters, using the estimated 3D landmarks and geometry as constraints in a
simple linear system. Qualitative modeling results are presented, as well as
quantitative evaluation of predicted 3D face landmarks in unconstrained video
sequences.
