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

ChildPlay: A New Benchmark for Understanding Children's Gaze Behaviour