  This paper describes our submission to the 1st 3D Face Alignment in the Wild
(3DFAW) Challenge. Our method builds upon the idea of convolutional part
heatmap regression [1], extending it for 3D face alignment. Our method
decomposes the problem into two parts: (a) X,Y (2D) estimation and (b) Z
(depth) estimation. At the first stage, our method estimates the X,Y
coordinates of the facial landmarks by producing a set of 2D heatmaps, one for
each landmark, using convolutional part heatmap regression. Then, these
heatmaps, alongside the input RGB image, are used as input to a very deep
subnetwork trained via residual learning for regressing the Z coordinate. Our
method ranked 1st in the 3DFAW Challenge, surpassing the second best result by
more than 22%.
