  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.
