  Gaze estimation is a fundamental task in many applications of computer
vision, human computer interaction and robotics. Many state-of-the-art methods
are trained and tested on custom datasets, making comparison across methods
challenging. Furthermore, existing gaze estimation datasets have limited head
pose and gaze variations, and the evaluations are conducted using different
protocols and metrics. In this paper, we propose a new gaze estimation dataset
called ETH-XGaze, consisting of over one million high-resolution images of
varying gaze under extreme head poses. We collect this dataset from 110
participants with a custom hardware setup including 18 digital SLR cameras and
adjustable illumination conditions, and a calibrated system to record ground
truth gaze targets. We show that our dataset can significantly improve the
robustness of gaze estimation methods across different head poses and gaze
angles. Additionally, we define a standardized experimental protocol and
evaluation metric on ETH-XGaze, to better unify gaze estimation research going
forward. The dataset and benchmark website are available at
https://ait.ethz.ch/projects/2020/ETH-XGaze
