UC Santa Cruz

Automatic synthesis of realistic eye images with pre- scribed gaze direction is important for multiple application domains. We introduce EyeGAN, an algorithm to generate eye images in the style of a desired target domain, that in- herit annotations available in images from a source domain. EyeGAN takes in input ternary masks, which are used as domain-independent proxies for gaze direction. We eval- uate EyeGAN against competing eye image synthesis al- gorithms by measuring a specific gaze consistency index. In addition, we present results from multiple experiments (involving eye region segmentation, pupil localization, and gaze direction estimation) showing that the use of EyeGAN- generated images with inherited annotations for network training leads to superior performances compared to other domain transfer algorithms.