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.

本文提出了一种改进全脸图像中注视和头部重定向的角度精度和真实性的方法，通过单目三维人脸重建的数据增强来扩展头部姿态和注视范围，以解决当前模型在处理大角度重定向时的限制。此外，我们还提出了一个能够在训练合成数据的情况下提供更好图像质量和未知对象身份保留的框架。实验证明，我们的方法在保持高图像质量的同时，显著提高了重定向角度精度，特别是在大角度重定向时。