  Appearance-based gaze estimation aims to predict the 3D eye gaze direction
from a single image. While recent deep learning-based approaches have
demonstrated excellent performance, they usually assume one calibrated face in
each input image and cannot output multi-person gaze in real time. However,
simultaneous gaze estimation for multiple people in the wild is necessary for
real-world applications. In this paper, we propose the first one-stage
end-to-end gaze estimation method, GazeOnce, which is capable of simultaneously
predicting gaze directions for multiple faces (>10) in an image. In addition,
we design a sophisticated data generation pipeline and propose a new dataset,
MPSGaze, which contains full images of multiple people with 3D gaze ground
truth. Experimental results demonstrate that our unified framework not only
offers a faster speed, but also provides a lower gaze estimation error compared
with state-of-the-art methods. This technique can be useful in real-time
applications with multiple users.
