Recent research has demonstrated the ability to estimate gaze on mobile
devices by performing inference on the image from the phone's front-facing
camera, and without requiring specialized hardware. While this offers wide
potential applications such as in human-computer interaction, medical diagnosis
and accessibility (e.g., hands free gaze as input for patients with motor
disorders), current methods are limited as they rely on collecting data from
real users, which is a tedious and expensive process that is hard to scale
across devices. There have been some attempts to synthesize eye region data
using 3D models that can simulate various head poses and camera settings,
however these lack in realism.
In this paper, we improve upon a recently suggested method, and propose a
generative adversarial framework to generate a large dataset of high resolution
colorful images with high diversity (e.g., in subjects, head pose, camera
settings) and realism, while simultaneously preserving the accuracy of gaze
labels. The proposed approach operates on extended regions of the eye, and even
completes missing parts of the image. Using this rich synthesized dataset, and
without using any additional training data from real users, we demonstrate
improvements over state-of-the-art for estimating 2D gaze position on mobile
devices. We further demonstrate cross-device generalization of model
performance, as well as improved robustness to diverse head pose, blur and
distance.

本文提出了一种用生成对抗网络生成大型合成数据集来进行注视估计的方法，该方法在移动设备上取得了最新成果并具有跨设备泛化能力。