Angle Range and Identity Similarity Enhanced Gaze and Head Redirection
  based on Synthetic data
Jiawei Qin Xueting Wang
In this paper, we propose a method for improving the angular accuracy and
photo-reality of gaze and head redirection in full-face images. The problem
with current models is that they cannot handle redirection at large angles, and
this limitation mainly comes from the lack of training data. To resolve this
problem, we create data augmentation by monocular 3D face reconstruction to
extend the head pose and gaze range of the real data, which allows the model to
handle a wider redirection range. In addition to the main focus on data
augmentation, we also propose a framework with better image quality and
identity preservation of unseen subjects even training with synthetic data.
Experiments show that our method significantly improves redirection performance
in terms of redirection angular accuracy while maintaining high image quality,
especially when redirecting to large angles.