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In this paper, we propose a method for improving the angular accuracy andphoto-reality of gaze and head redirection in full-face images. The problemwith current models is that they cannot handle redirection at large angles, andthis limitation mainly comes from the lack of training data. To resolve thisproblem, we create data augmentation by monocular 3D face reconstruction toextend the head pose and gaze range of the real data, which allows the model tohandle a wider redirection range. In addition to the main focus on dataaugmentation, we also propose a framework with better image quality andidentity preservation of unseen subjects even training with synthetic data.Experiments show that our method significantly improves redirection performancein terms of redirection angular accuracy while maintaining high image quality,especially when redirecting to large angles.

Angle Range and Identity Similarity Enhanced Gaze and Head Redirection based on Synthetic data