  Gaze redirection is the task of changing the gaze to a desired direction for
a given monocular eye patch image. Many applications such as videoconferencing,
films, games, and generation of training data for gaze estimation require
redirecting the gaze, without distorting the appearance of the area surrounding
the eye and while producing photo-realistic images. Existing methods lack the
ability to generate perceptually plausible images. In this work, we present a
novel method to alleviate this problem by leveraging generative adversarial
training to synthesize an eye image conditioned on a target gaze direction. Our
method ensures perceptual similarity and consistency of synthesized images to
the real images. Furthermore, a gaze estimation loss is used to control the
gaze direction accurately. To attain high-quality images, we incorporate
perceptual and cycle consistency losses into our architecture. In extensive
evaluations we show that the proposed method outperforms state-of-the-art
approaches in terms of both image quality and redirection precision. Finally,
we show that generated images can bring significant improvement for the gaze
estimation task if used to augment real training data.
