  Estimation of facial shapes plays a central role for face transfer and
animation. Accurate 3D face reconstruction, however, often deploys iterative
and costly methods preventing real-time applications. In this work we design a
compact and fast CNN model enabling real-time face reconstruction on mobile
devices. For this purpose, we first study more traditional but slow morphable
face models and use them to automatically annotate a large set of images for
CNN training. We then investigate a class of efficient MobileNet CNNs and adapt
such models for the task of shape regression. Our evaluation on three datasets
demonstrates significant improvements in the speed and the size of our model
while maintaining state-of-the-art reconstruction accuracy.
