Face aging (FA) for young faces refers to rendering the aging faces at target age for an individual, generally under 20s, which is an important topic of facial age analysis. Unlike traditional FA for adults, it is challenging to age children with one deep learning-based FA network, since there are deformations of facial shapes and variations of textural details. To alleviate the deficiency, a unified FA framework for young faces is proposed, which consists of two decoupled networks to apply aging image translation. It explicitly models transformations of geometry and appearance using two components: GD-GAN, which simulates the Geometric Deformation using Generative Adversarial Network; TV-GAN, which simulates the Textural Variations guided by the age-related saliency map. Extensive experiments demonstrate that our method has advantages over the state-of-the-art methods in terms of synthesizing visually plausible images for young faces, as well as preserving the personalized features.

年轻面孔的面部老化（FA）是指在目标年龄（通常在20秒以下）下渲染个人的老化面孔，这是面部年龄分析的重要主题。与成人的传统FA不同，通过基于深度学习的FA网络对孩子进行年龄挑战是有挑战性的，因为面部形状会发生变形，并且纹理细节也会有所变化。为了缓解这一不足，提出了一个针对年轻人脸的统一FA框架，该框架由两个解耦网络组成，用于应用衰老图像转换。它使用两个组件显式地对几何形状和外观的转换进行建模：GD-GAN，它使用生成对抗网络模拟了几何变形；卫视，它模拟了与年龄相关的显着性图指导的纹理变化。大量实验表明，在为年轻面孔合成视觉上合理的图像以及保留个性化功能方面，我们的方法比现有技术具有优势。