  Gaze behaviors such as eye-contact or shared attention are important markers
for diagnosing developmental disorders in children. While previous studies have
looked at some of these elements, the analysis is usually performed on private
datasets and is restricted to lab settings. Furthermore, all publicly available
gaze target prediction benchmarks mostly contain instances of adults, which
makes models trained on them less applicable to scenarios with young children.
In this paper, we propose the first study for predicting the gaze target of
children and interacting adults. To this end, we introduce the ChildPlay
dataset: a curated collection of short video clips featuring children playing
and interacting with adults in uncontrolled environments (e.g. kindergarten,
therapy centers, preschools etc.), which we annotate with rich gaze
information. We further propose a new model for gaze target prediction that is
geometrically grounded by explicitly identifying the scene parts in the 3D
field of view (3DFoV) of the person, leveraging recent geometry preserving
depth inference methods. Our model achieves state of the art results on
benchmark datasets and ChildPlay. Furthermore, results show that looking at
faces prediction performance on children is much worse than on adults, and can
be significantly improved by fine-tuning models using child gaze annotations.
Our dataset and models will be made publicly available.
