We propose a novel approach that addresses face aging as an unsupervised image-to-image translation problem. The proposed approach achieves age progression (i.e., future looks) and regression (i.e., previous looks) of face images that belong to a specific age class by translating them to other (subsequent or precedent) age classes. It learns pairwise translations between all age classes. Two variants are presented. The first one learns a global transformation, while the second one incorporates a pyramid encoding and decoding scheme to more effectively diffuse age class information. The proposed variants are thoroughly evaluated with respect to both qualitative and quantitative criteria. They yield appealing face age progression and regression results when compared to ground truth images and outperform state-of-the-art approaches for face aging based on quantitative evaluation metrics. Notably, the incorporation of pyramid encoding and decoding is proven to be beneficial to the quality of the generated images.

我们提出了一种新颖的方法来解决人脸老化问题，将其作为无监督的图像到图像转换问题。所提出的方法通过将属于特定年龄类别的面部图像转换成其他（后续或先前）年龄类别来实现年龄进展（即，未来外观）和回归（即，以前的外观）。它学习所有年龄段之间的成对翻译。提出了两种变体。第一个学习全局转换，而第二个结合金字塔编码和解码方案，以更有效地传播年龄信息。对于定性和定量标准，对所建议的变体进行了全面评估。与地面真实图像相比，它们可产生吸引人的面部年龄进展和回归结果，并且优于基于定量评估指标的最先进的面部衰老方法。值得注意的是，金字塔编码和解码的结合被证明对所生成图像的质量是有益的。