ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation

By Xucong Zhang, Seonwook Park, Thabo Beeler, Derek Bradley, Siyu Tang, Otmar Hilliges. 2020

362 citations recorded by the source corpus.

Gaze estimation is a fundamental task in many applications of computer vision, human computer interaction and robotics. Many state-of-the-art methods are trained and tested on custom datasets, making comparison across methods challenging. Furthermore, existing gaze estimation datasets have limited head pose and gaze variations, and the evaluations are conducted using different protocols and metrics. In this paper, we propose a new gaze estimation dataset called ETH-XGaze, consisting of over one million high-resolution images of varying gaze under extreme head poses. We collect this dataset from 110 participants with a custom hardware setup including 18 digital SLR cameras and adjustable illumination conditions, and a calibrated system to record ground truth gaze targets. We show that our dataset can significantly improve the robustness of gaze estimation methods across different head poses and gaze angles. Additionally, we define a standardized experimental protocol and evaluation metric on ETH-XGaze, to better unify gaze estimation research going forward. The dataset and benchmark website are available at this https URL

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, 3D Vision & Pose, Human-Computer Interaction

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Citations in the corpus

StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets (2022, background): “Current large-scale, high-resolution datasets are of single object classes or contain many similar images [Fregin et al. 2018; Perot et al. 2020; Zhang et al. 2020].”