Since it is difficult to collect face images of the same subject over a long
range of age span, most existing face aging methods resort to unpaired datasets
to learn age mappings. However, the matching ambiguity between young and aged
face images inherent to unpaired training data may lead to unnatural changes of
facial attributes during the aging process, which could not be solved by only
enforcing identity consistency like most existing studies do. In this paper, we
propose a attribute-aware face aging model with wavelet-based Generative
Adversarial Networks (GANs) to address the above issues. To be specific, we
embed facial attribute vectors into both generator and discriminator of the
model to encourage each synthesized elderly face image to be faithful to the
attribute of its corresponding input. In addition, a wavelet packet transform
(WPT) module is incorporated to improve the visual fidelity of generated images
by capturing age-related texture details at multiple scales in the frequency
space. Qualitative results demonstrate the ability of our model to synthesize
visually plausible face images, and extensive quantitative evaluation results
show that the proposed method achieves state-of-the-art performance on existing
datasets.

使用基于小波的生成对抗网络与面部属性向量来提高合成面部图像的质量，能够更准确地呈现面部随着时间推移带来的变化，并在现有数据集上取得了最先进的表现。