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

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

31 Jul 2020ECCV 2020 8arXiv:2007.15837archive 2025-07-28

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

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 https://ait.ethz.ch/projects/2020/ETH-XGaze

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xucong-zhang/ETH-XGaze officialpytorch report

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Tasks

Gaze Estimation

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ETH-XGaze

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Gaze Estimation ETH-XGaze ETHXGaze Angular Error 4.7 #1 of 1 Archive leaderboard report
Gaze Estimation MPSGaze ResNet18 Angular Error 8.1 #1 of 1 Archive leaderboard report

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