  Understanding where people are looking is an informative social cue. In this
work, we present Gaze360, a large-scale gaze-tracking dataset and method for
robust 3D gaze estimation in unconstrained images. Our dataset consists of 238
subjects in indoor and outdoor environments with labelled 3D gaze across a wide
range of head poses and distances. It is the largest publicly available dataset
of its kind by both subject and variety, made possible by a simple and
efficient collection method. Our proposed 3D gaze model extends existing models
to include temporal information and to directly output an estimate of gaze
uncertainty. We demonstrate the benefits of our model via an ablation study,
and show its generalization performance via a cross-dataset evaluation against
other recent gaze benchmark datasets. We furthermore propose a simple
self-supervised approach to improve cross-dataset domain adaptation. Finally,
we demonstrate an application of our model for estimating customer attention in
a supermarket setting. Our dataset and models are available at
http://gaze360.csail.mit.edu .
