Face re-aging is a prominent field in computer vision and graphics, with significant applications in photorealistic domains such as movies, advertising, and live streaming. Recently, the need to apply face re-aging to non-photorealistic images, like comics, illustrations, and animations, has emerged as an extension in various entertainment sectors. However, the lack of a network that can seamlessly edit the apparent age in NPR images has limited these tasks to a naive, sequential approach. This often results in unpleasant artifacts and a loss of facial attributes due to domain discrepancies. In this paper, we introduce a novel one-stage method for face re-aging combined with portrait style transfer, executed in a single generative step. We leverage existing face re-aging and style transfer networks, both trained within the same PR domain. Our method uniquely fuses distinct latent vectors, each responsible for managing aging-related attributes and NPR appearance. By adopting an exemplar-based approach, our method offers greater flexibility compared to domain-level fine-tuning approaches, which typically require separate training or fine-tuning for each domain. This effectively addresses the limitation of requiring paired datasets for re-aging and domain-level, data-driven approaches for stylization. Our experiments show that our model can effortlessly generate re-aged images while simultaneously transferring the style of examples, maintaining both natural appearance and controllability.

翻译：人脸重龄化是计算机视觉与图形学领域的重要研究方向，在电影、广告、直播等逼真图像领域具有显著应用价值。近年来，将人脸重龄化技术扩展到非逼真图像（如漫画、插画、动画）的需求在各类娱乐产业中日益显现。然而，由于缺乏能够无缝编辑非逼真渲染（NPR）图像中表观年龄的网络，现有方案只能采用简单的顺序处理方式，这常因域间差异导致令人不悦的伪影和面部属性丢失。本文提出一种创新的一阶段方法，通过单次生成步骤同时完成人脸重龄化与肖像风格迁移。我们利用已在同一逼真渲染（PR）域中完成训练的现有人脸重龄化与风格迁移网络，创新性地融合了分别负责年龄属性管理和NPR外观表征的独立潜在向量。相较于需要为每个域单独训练或微调的域级精调方法，本文采用的范例驱动方法具备更高灵活性，有效解决了重龄化需要配对数据集、风格化需要域级数据驱动方法的局限性。实验表明，本模型能够在自然保留面部属性与可控性的同时，轻松生成重龄化图像并同步迁移范例风格。