Existing face relighting methods often struggle with two problems:
maintaining the local facial details of the subject and accurately removing and
synthesizing shadows in the relit image, especially hard shadows. We propose a
novel deep face relighting method that addresses both problems. Our method
learns to predict the ratio (quotient) image between a source image and the
target image with the desired lighting, allowing us to relight the image while
maintaining the local facial details. During training, our model also learns to
accurately modify shadows by using estimated shadow masks to emphasize on the
high-contrast shadow borders. Furthermore, we introduce a method to use the
shadow mask to estimate the ambient light intensity in an image, and are thus
able to leverage multiple datasets during training with different global
lighting intensities. With quantitative and qualitative evaluations on the
Multi-PIE and FFHQ datasets, we demonstrate that our proposed method faithfully
maintains the local facial details of the subject and can accurately handle
hard shadows while achieving state-of-the-art face relighting performance.

我们提出了一种新颖的深度人脸光照方法，通过学习预测源图像和目标图像之间的比值图像，同时使用估计的阴影掩码准确修改影子，使得我们能够在保留本地面部细节的同时重新照明图像，并使用阴影掩码来估算图像中的环境光强度，在 Multi-PIE 和 FFHQ 数据集上进行定量和定性评估，证明了我们所提出的方法在保留主体的本地面部细节，并能够准确地处理深硬阴影的同时达到了最先进的人脸光照效果。