ToonAging：艺术肖像风格迁移下的面部再老化
ToonAging: Face Re-Aging upon Artistic Portrait Style Transfer
AI总结：
提出一种单阶段方法，将面部再老化与艺术肖像风格迁移结合，通过融合潜在向量在单一生成步骤中实现，避免顺序处理的伪影，无需配对数据集，保持自然外观和可控性。
摘要：
面部再老化是计算机视觉和图形学中的一个重要领域，在电影、广告和直播等照片级真实感领域具有重要应用。最近，将面部再老化应用于非照片级真实感图像（如漫画、插图和动画）的需求，作为各种娱乐行业的延伸而出现。然而，缺乏能够无缝编辑NPR图像中表观年龄的网络，使得这些任务局限于一种朴素的顺序处理方法。这通常会导致令人不快的伪影和由于领域差异而导致的面部属性丢失。在本文中，我们提出了一种新颖的单阶段方法，将面部再老化与肖像风格迁移相结合，在单个生成步骤中执行。我们利用现有的面部再老化和风格迁移网络，两者都在相同的PR领域内训练。我们的方法独特地融合了不同的潜在向量，每个向量负责管理老化相关属性和NPR外观。通过采用基于范例的方法，我们的方法比领域级微调方法提供了更大的灵活性，后者通常需要为每个领域进行单独训练或微调。这有效地解决了需要配对数据集进行再老化和领域级、数据驱动方法进行风格化的局限性。我们的实验表明，我们的模型可以轻松生成再老化图像，同时迁移示例的风格，保持自然外观和可控性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
