  We present an end-to-end head-pose estimation network designed to predict
Euler angles through the full range head yaws from a single RGB image. Existing
methods perform well for frontal views but few target head pose from all
viewpoints. This has applications in autonomous driving and retail. Our network
builds on multi-loss approaches with changes to loss functions and training
strategies adapted to wide range estimation. Additionally, we extract ground
truth labelings of anterior views from a current panoptic dataset for the first
time. The resulting Wide Headpose Estimation Network (WHENet) is the first
fine-grained modern method applicable to the full-range of head yaws (hence
wide) yet also meets or beats state-of-the-art methods for frontal head pose
estimation. Our network is compact and efficient for mobile devices and
applications.
