# Photo-Realistic Monocular Gaze Redirection Using Generative Adversarial Networks

## TL;DR

This paper introduces a novel GAN-based method for photo-realistic monocular gaze redirection that maintains appearance and improves gaze estimation accuracy, outperforming existing approaches in quality and precision.

## Contribution

The work presents a new GAN framework with perceptual, cycle consistency, and gaze estimation losses for high-quality gaze redirection, enhancing both image realism and accuracy.

## Key findings

- Outperforms state-of-the-art in image quality and gaze redirection precision
- Generated images improve gaze estimation accuracy when used for data augmentation
- Method ensures perceptual similarity and gaze control in synthesized images

## Abstract

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.