  Gaze estimation methods estimate gaze from facial appearance with a single
camera. However, due to the limited view of a single camera, the captured
facial appearance cannot provide complete facial information and thus
complicate the gaze estimation problem. Recently, camera devices are rapidly
updated. Dual cameras are affordable for users and have been integrated in many
devices. This development suggests that we can further improve gaze estimation
performance with dual-view gaze estimation. In this paper, we propose a
dual-view gaze estimation network (DV-Gaze). DV-Gaze estimates dual-view gaze
directions from a pair of images. We first propose a dual-view interactive
convolution (DIC) block in DV-Gaze. DIC blocks exchange dual-view information
during convolution in multiple feature scales. It fuses dual-view features
along epipolar lines and compensates for the original feature with the fused
feature. We further propose a dual-view transformer to estimate gaze from
dual-view features. Camera poses are encoded to indicate the position
information in the transformer. We also consider the geometric relation between
dual-view gaze directions and propose a dual-view gaze consistency loss for
DV-Gaze. DV-Gaze achieves state-of-the-art performance on ETH-XGaze and EVE
datasets. Our experiments also prove the potential of dual-view gaze
estimation. We release codes in https://github.com/yihuacheng/DVGaze.
