How can one visually characterize people in a decade? In this work, we assemble the Faces Through Time dataset, which contains over a thousand portrait images from each decade, spanning the 1880s to the present day. Using our new dataset, we present a framework for resynthesizing portrait images across time, imagining how a portrait taken during a particular decade might have looked like, had it been taken in other decades. Our framework optimizes a family of per-decade generators that reveal subtle changes that differentiate decade--such as different hairstyles or makeup--while maintaining the identity of the input portrait. Experiments show that our method is more effective in resynthesizing portraits across time compared to state-of-the-art image-to-image translation methods, as well as attribute-based and language-guided portrait editing models. Our code and data will be available at https://facesthroughtime.github.io

翻译：如何通过视觉特征来刻画不同年代的人群？为此，我们构建了"时光面孔"数据集，该数据集包含从19世纪80年代至今每个年代超过千张的肖像图像。基于此新数据集，我们提出了一套跨年代肖像再合成框架，能够模拟给定年代拍摄的肖像在其他年代可能呈现的面貌。该框架优化了一组逐年代生成器，在保持输入肖像身份特征的同时，揭示发型、妆容等区分不同年代的细微变化。实验表明，与当前最先进的图像到图像转换方法、基于属性的肖像编辑模型以及语言引导的肖像编辑模型相比，我们的方法在跨年代肖像再合成任务中表现更优。相关代码与数据集已开源至https://facesthroughtime.github.io