GazeOnce: Real-Time Multi-Person Gaze Estimation
This addresses the need for simultaneous gaze estimation for multiple people in real-world applications, representing a novel method for a known bottleneck.
The paper tackles the problem of real-time multi-person gaze estimation from a single image, proposing GazeOnce, a one-stage end-to-end method that simultaneously predicts gaze directions for over 10 faces with faster speed and lower error compared to state-of-the-art methods.
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.