EyeGAN: Gaze–Preserving, Mask–Mediated Eye Image Synthesis
Harsimran Kaur, Roberto Manduchi · 2020
Automatic synthesis of realistic eye images with prescribed 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 inherit 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 evaluate EyeGAN against competing eye image synthesis algorithms 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 EyeGANgenerated images with inherited annotations for network training leads to superior performances compared to other domain transfer algorithms.