  Images of the eye are key in several computer vision problems, such as shape
registration and gaze estimation. Recent large-scale supervised methods for
these problems require time-consuming data collection and manual annotation,
which can be unreliable. We propose synthesizing perfectly labelled
photo-realistic training data in a fraction of the time. We used computer
graphics techniques to build a collection of dynamic eye-region models from
head scan geometry. These were randomly posed to synthesize close-up eye images
for a wide range of head poses, gaze directions, and illumination conditions.
We used our model's controllability to verify the importance of realistic
illumination and shape variations in eye-region training data. Finally, we
demonstrate the benefits of our synthesized training data (SynthesEyes) by
out-performing state-of-the-art methods for eye-shape registration as well as
cross-dataset appearance-based gaze estimation in the wild.
