Abstract. Estimating human gaze from natural eye images only is a
challenging task. Gaze direction can be defined by the pupil- and the
eyeball center where the latter is unobservable in 2D images. Hence,
achieving highly accurate gaze estimates is an ill-posed problem. In this
paper, we introduce a novel deep neural network architecture specifically
designed for the task of gaze estimation from single eye input. Instead
of directly regressing two angles for the pitch and yaw of the eyeball,
we regress to an intermediate pictorial representation which in turn simplifies the task of 3D gaze direction estimation. Our quantitative and
qualitative results show that our approach achieves higher accuracies
than the state-of-the-art and is robust to variation in gaze, head pose
and image quality