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.

翻译：人脸重新老化是计算机视觉与图形学领域的重要研究方向，在影视、广告、直播等照片级真实感领域具有广泛应用。近年来，随着娱乐产业延伸发展，将人脸重新老化应用于漫画、插画、动画等非照片级真实感图像的需求日益凸显。然而，由于缺乏能够无缝编辑非照片级真实感图像表观年龄的网络，现有方法仅能采用顺序执行各任务的朴素方案，导致因领域差异而产生令人不快的伪影并损失面部特征。本文提出一种结合肖像风格迁移的人脸重新老化单阶段新方法，通过单一生成步骤即可完成操作。我们利用已在同一照片级真实感领域训练的人脸重新老化与风格迁移网络，创新性地融合了分别控制老化属性与非照片级真实感外观的独立潜在向量。采用基于范例的方法，本方法较通常需为每个领域单独训练或微调的领域级微调方式具有更高灵活性，有效解决了重新老化所需配对数据集与风格化所需领域级数据驱动方法之间的固有限制。实验表明，本模型在保持自然外观与可控性的前提下，能轻松生成重新老化图像并同步迁移范例风格。