MyTimeMachine：个性化面部年龄转换
MyTimeMachine: Personalized Facial Age Transformation
AI总结：
提出MyTimeMachine方法，结合全局衰老先验与个人照片集（少至50张图像），通过Adapter Network和三个损失函数实现个性化年龄转换，并扩展至视频，生成高质量、身份一致且时间连贯的衰老效果。
摘要：
面部衰老是一个复杂的过程，高度依赖于性别、种族、生活方式等多种因素，这使得学习一个全局衰老先验来准确预测任何个体的衰老情况极具挑战性。现有技术通常能产生逼真且合理的衰老结果，但重新衰老后的图像往往与该人在目标年龄时的外貌并不相似，因此需要进行个性化处理。在许多虚拟衰老的实际应用中，例如电影和电视节目中的VFX，通常可以获取用户在较短时间间隔（20~40年）内描绘衰老过程的个人照片集。然而，直接尝试在个人照片集上对全局衰老技术进行个性化的简单方法往往会失败。因此，我们提出了MyTimeMachine（MyTM），它将全局衰老先验与个人照片集（使用少至50张图像）相结合，以学习个性化的年龄转换。我们引入了一种新颖的Adapter Network，将个性化衰老特征与全局衰老特征相结合，并使用StyleGAN2生成重新衰老的图像。我们还引入了三个损失函数，通过个性化衰老损失、外推正则化和自适应w-norm正则化来个性化Adapter Network。我们的方法还可以扩展到视频中，实现高质量、保持身份一致性且时间一致的衰老效果，与目标年龄时的实际外貌高度相似，证明了其优于现有最先进方法的优越性。
英文摘要：
Facial aging is a complex process, highly dependent on multiple factors like gender, ethnicity, lifestyle, etc., making it extremely challenging to learn a global aging prior to predict aging for any individual accurately. Existing techniques often produce realistic and plausible aging results, but the re-aged images often do not resemble the person's appearance at the target age and thus need personalization. In many practical applications of virtual aging, e.g. VFX in movies and TV shows, access to a personal photo collection of the user depicting aging in a small time interval (20$\sim$40 years) is often available. However, naive attempts to personalize global aging techniques on personal photo collections often fail. Thus, we propose MyTimeMachine (MyTM), which combines a global aging prior with a personal photo collection (using as few as 50 images) to learn a personalized age transformation. We introduce a novel Adapter Network that combines personalized aging features with global aging features and generates a re-aged image with StyleGAN2. We also introduce three loss functions to personalize the Adapter Network with personalized aging loss, extrapolation regularization, and adaptive w-norm regularization. Our approach can also be extended to videos, achieving high-quality, identity-preserving, and temporally consistent aging effects that resemble actual appearances at target ages, demonstrating its superiority over state-of-the-art approaches.
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。
