论文标题
老化木马（AMGAN）：高分辨率可控的面部老化，有条件gan
AgingMapGAN (AMGAN): High-Resolution Controllable Face Aging with Spatially-Aware Conditional GANs
论文作者
论文摘要
面部衰老的现有方法和数据集产生的结果偏向于平均值，单个变化和表达皱纹通常是看不见或忽略的，而偏向于全球模式，例如面部的肥大。此外，它们几乎无法控制面孔的老化方式，并且很难将其缩放到大图像，从而阻止了它们在许多现实世界中的用法。为了解决这些局限性，我们提出了一种方法，可以使用特定于种族的老龄化信息和弱空间监督来改变高分辨率图像的外观，以指导老化过程。我们证明了我们提出的方法在质量，控制以及如何在高清图像上使用的优势，同时限制了计算开销。
Existing approaches and datasets for face aging produce results skewed towards the mean, with individual variations and expression wrinkles often invisible or overlooked in favor of global patterns such as the fattening of the face. Moreover, they offer little to no control over the way the faces are aged and can difficultly be scaled to large images, thus preventing their usage in many real-world applications. To address these limitations, we present an approach to change the appearance of a high-resolution image using ethnicity-specific aging information and weak spatial supervision to guide the aging process. We demonstrate the advantage of our proposed method in terms of quality, control, and how it can be used on high-definition images while limiting the computational overhead.
