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

• URL: http://arxiv.org/abs/2502.02307v2
• Date: Thu, 13 Mar 2025 15:59:03 GMT
• Title: UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training
• Authors: Jiawei Qin, Xucong Zhang, Yusuke Sugano,
• Abstract summary: We propose UniGaze, leveraging large-scale in-the-wild facial datasets for gaze estimation through self-supervised pre-training.<n>Our experiments reveal that self-supervised approaches designed for semantic tasks fail when applied to gaze estimation.<n>We demonstrate that UniGaze significantly improves generalization across multiple data domains while minimizing reliance on costly labeled data.
• Score: 12.680014448486242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 shown remarkable performances in generalization across various vision tasks. However, their effectiveness in gaze estimation remains unexplored. We propose UniGaze, for the first time, leveraging large-scale in-the-wild facial datasets for gaze estimation through self-supervised pre-training. Through systematic investigation, we clarify critical factors that are essential for effective pretraining 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 challenging cross-dataset evaluation 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.

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