  As a critical cue for understanding human intention, human gaze provides a
key signal for Human-Computer Interaction(HCI) applications. Appearance-based
gaze estimation, which directly regresses the gaze vector from eye images, has
made great progress recently based on Convolutional Neural Networks(ConvNets)
architecture and open-source large-scale gaze datasets. However, encoding
model-based knowledge into CNN model to further improve the gaze estimation
performance remains a topic that needs to be explored. In this paper, we
propose HybridGazeNet(HGN), a unified framework that encodes the geometric
eyeball model into the appearance-based CNN architecture explicitly. Composed
of a multi-branch network and an uncertainty module, HybridGazeNet is trained
using a hyridized strategy. Experiments on multiple challenging gaze datasets
shows that HybridGazeNet has better accuracy and generalization ability
compared with existing SOTA methods. The code will be released later.
