Abstract
Although appearance-based point-of-gaze (PoG) estimation has improved, the estimators still struggle to generalize across individuals due to personal differences. Therefore, person-specific calibration is required for accurate PoG estimation. However, calibrated PoG estimators are often sensitive to head pose variations. To address this, we investigate the key factors influencing calibrated estimators and explore pose-robust calibration strategies. Specifically, we first construct a benchmark, MobilePoG, which includes facial images from 32 individuals focusing on designated points under either fixed or continuously changing head poses. Using this benchmark, we systematically analyze how the diversity of calibration points and head poses influences estimation accuracy. Our experiments show that introducing a wider range of head poses during calibration improves the estimator's ability to handle pose variation. Building on this insight, we propose a dynamic calibration strategy in which users fixate on calibration points while moving their phones. This strategy naturally introduces head pose variation during a user-friendly and efficient calibration process, ultimately producing a better calibrated PoG estimator that is less sensitive to head pose variations than those using conventional calibration strategies. Codes and datasets are available at our project page.
Abstract (translated)
尽管基于外观的注视点（PoG，Point of Gaze）估计已经得到了改进，但由于个人差异的存在，估算器仍然难以在不同个体之间进行泛化。因此，为了实现准确的PoG估计，需要针对特定个体进行校准。然而，经过校准的PoG估算器通常对头部姿态的变化很敏感。为了解决这一问题，我们研究了影响校准估算器的关键因素，并探索了能够抵抗姿态变化的校准策略。 具体而言，我们首先构建了一个基准测试集MobilePoG，其中包含32个个体在固定或持续变化的头部姿态下注视指定点时采集到的脸部图像。利用这一基准测试集，我们系统地分析了校准点的多样性以及头部姿态对估计准确性的影响。我们的实验表明，在校准时引入更广泛的头部姿态范围可以提高估算器处理姿势变化的能力。 基于此洞察，我们提出了一种动态校准策略：用户在移动手机的同时注视着校准点。这种策略自然地在用户体验友好且高效的校准过程中引入了头部姿态的变化，最终产生了一个对头部姿态变化不那么敏感的更好的PoG校准估算器。代码和数据集可以在我们的项目页面上获取。
URL
https://arxiv.org/abs/2508.10268
