ChildPlay: A New Benchmark for Understanding Children's Gaze Behaviour
Samy Tafasca, Anshul Gupta, Jean-Marc Odobez

TL;DR
This paper introduces ChildPlay, a novel dataset and model for predicting children's gaze behavior in natural settings, addressing the gap in existing benchmarks focused on adults and lab environments.
Contribution
It provides the first dataset and model specifically designed for understanding gaze behavior in children and interacting adults in uncontrolled environments.
Findings
The model achieves state-of-the-art results on ChildPlay and existing benchmarks.
Gaze prediction on children is less accurate than on adults but improves with fine-tuning.
Child-specific gaze models outperform adult-trained models when adapted to children.
Abstract
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…
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Taxonomy
TopicsGaze Tracking and Assistive Technology · Craniofacial Disorders and Treatments · Domain Adaptation and Few-Shot Learning
