UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training

Abstract

Despite decades of research on data collection and model architectures, current gaze estimation models encounter significant challenges in generalizing across diverse data domains. Recent advances in self-supervised pre-training have demonstrated remarkable generalization across various vision tasks. However, their effectiveness in gaze estimation remains unexplored. We propose UniGaze, which for the first time leverages large-scale in-the-wild facial datasets for gaze estimation through self-supervised pre-training. Through systematic investigation, we clarify critical factors essential for effective pre-training in gaze estimation. Our experiments reveal that self-supervised approaches designed for semantic tasks fail when applied to gaze estimation, while our carefully designed pre-training pipeline consistently improves cross-domain performance. Through comprehensive experiments of the challenging cross-dataset evaluations and novel protocols, including leave-one-dataset-out and joint-dataset settings, we demonstrate that UniGaze significantly improves generalization across multiple data domains while minimizing reliance on costly labeled data. Source code and model are available at https://github.com/ut-vision/UniGaze.