Although recent deep learning based gaze estimation approaches have achieved much improvement, we still know little about how gaze features are connected to the physics of gaze. In this paper, we try to answer this question by analyzing the gaze feature manifold. Our analysis revealed the insight that the geodesic distance between gaze features is consistent with the gaze differences between samples. According to this finding, we construct the Physics- Consistent Feature (PCF) in an analytical way, which connects gaze feature to the physical definition of gaze. We further propose the PCFGaze framework that directly optimizes gaze feature space by the guidance of PCF. Experimental results demonstrate that the proposed framework alleviates the overfitting problem and significantly improves cross-domain gaze estimation accuracy without extra training data. The insight of gaze feature has the potential to benefit other regression tasks with physical meanings.

翻译：尽管近年来基于深度学习的注视估计方法取得了显著进展，但对于注视特征如何与注视物理学建立联系仍知之甚少。本文通过分析注视特征流形试图回答这一问题。我们的分析揭示了如下关键见解：注视特征之间的测地距离与样本间的注视差异保持一致。基于这一发现，我们以解析方式构建了物理一致性特征（Physics-Consistent Feature, PCF），将注视特征与注视的物理定义联系起来。进一步地，我们提出PCFGaze框架，通过PCF的引导直接优化注视特征空间。实验结果表明，该框架减轻了过拟合问题，并在无需额外训练数据的情况下显著提升了跨域注视估计的准确率。对注视特征的这一认识有望为其他具有物理意义的回归任务带来助益。