  Deep learning has been impressively successful in the last decade in
predicting human head poses from monocular images. For in-the-wild inputs, the
research community has predominantly relied on a single training set of
semi-synthetic nature. This paper suggest the combination of different flavors
of synthetic data in order to achieve better generalization to natural images.
Moreover, additional expansion of the data volume using traditional
out-of-plane rotation synthesis is considered. Together with a novel
combination of losses and a network architecture with a standard
feature-extractor, a competitive model is obtained, both in accuracy and
efficiency, which allows full 6 DoF pose estimation in practical real-time
applications.
