Abstract Age synthesis methods typically take a single image as input and use a specific number to control the age of the generated image. In this paper, we propose a novel framework taking two images as inputs, named dual-reference age synthesis (DRAS), which approaches the task differently; instead of using “hard” age information, i.e. a fixed number, our model determines the target age in a “soft” way, by employing a second reference image. Specifically, the proposed framework consists of an identity agent, an age agent and a generative adversarial network. It takes two images as input – an identity reference and an age reference – and outputs a new image that shares corresponding features with each. Experimental results on two benchmark datasets (UTKFace and CACD) demonstrate the appealing performance and flexibility of the proposed framework.

中文翻译：

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双参考年龄综合

摘要 年龄合成方法通常以单个图像作为输入，并使用特定数字来控制生成图像的年龄。在本文中，我们提出了一种以两张图像为输入的新框架，称为双参考年龄合成（DRAS），它以不同的方式处理任务；我们的模型没有使用“硬”年龄信息，即固定数字，而是通过使用第二个参考图像以“软”方式确定目标年龄。具体来说，所提出的框架由身份代理、年龄代理和生成对抗网络组成。它以两张图像作为输入——一个身份参考和一个年龄参考——并输出一个新图像，每个图像共享相应的特征。在两个基准数据集（UTKFace 和 CACD）上的实验结果证明了所提出框架的吸引人的性能和灵活性。