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.

翻译：本文提出了一种提升全脸图像中注视与头部重定向角度精度及照片真实感的方法。当前模型难以处理大角度重定向，其限制主要源于训练数据的匮乏。为解决此问题，我们通过单目三维人脸重建进行数据增强，扩展了真实数据的头部姿态与注视范围，使模型能够处理更广泛的重定向角度。除了重点进行数据增强外，我们还提出了一个框架，可在使用合成数据训练的情况下，改善未见主体的图像质量与身份保持能力。实验表明，我们的方法在重定向角度精度方面显著提升了重定向性能，同时保持了较高的图像质量，尤其是在大角度重定向场景中。