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：人脸年龄重编辑是计算机视觉与图形学中的重要领域，在电影、广告和直播等写实领域具有广泛应用。近年来，将人脸年龄重编辑应用于漫画、插画和动画等非写实图像的需求，已作为各类娱乐产业的延伸方向逐渐显现。然而，由于缺乏能够无缝编辑非写实图像表观年龄的网络，此类任务目前仅限于简单的串行处理方式。这常因领域差异导致不自然的伪影及面部属性丢失。本文提出一种结合肖像风格迁移的单阶段人脸年龄重编辑方法，通过单次生成步骤完成处理。我们利用在相同写实领域训练的现有人脸年龄重编辑网络与风格迁移网络，创新性地融合了分别负责管理年龄相关属性与非写实外观的隐向量。通过采用基于范例的方法，本方案相较于通常需要针对每个领域单独训练或微调的领域级微调方法具有更高灵活性，有效解决了年龄重编辑需要配对数据集、风格化需要领域级数据驱动方法的需求限制。实验表明，我们的模型能够轻松生成年龄重编辑图像，同时迁移范例风格，并保持自然外观与可控性。