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 absence
of a network capable of seamlessly editing the apparent age on NPR images means
that these tasks have been confined to a naive approach, applying each task
sequentially. 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. Adopting an exemplar-based
approach, our method offers greater flexibility than 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.

在本文中，我们引入了一种新颖的一阶段面部恢复年轻化方法，并结合肖像风格转换，在单个生成步骤中执行。我们利用现有的面部恢复年轻化和风格转换网络，两者均在相同的 PR 领域内进行训练。我们的方法独特地融合了不同的潜在向量，每个向量负责管理与年龄相关的属性和 NPR 外观。采用示例为基础的方法，我们的方法比领域级精调方法提供了更大的灵活性，这些方法通常需要针对每个领域进行单独训练或精调。我们的实验表明，我们的模型可以轻松生成年轻化的图像，同时保持示例的风格，保持自然外观和可控性。