Facilitating Continuous Facial Aging Through Latent Age Attribute Modulation

In recent years, facial aging has attracted significant research interest due to its broad applications and potential benefits. While generative adversarial networks (GANs) have achieved notable progress in synthesizing realistic facial images, many GAN-based facial aging methods struggle to accurately capture the continuous progression of age-related changes over time. In this article, we propose an innovative framework featuring the latent age attribute module (LAAM), which maps age attributes to a structured latent space that facilitates efficient sampling for precise age attribute modeling. We further introduce the age-AdaIN fusion module (AFM), which seamlessly integrates age features from LAAM with facial content features, enabling the generation of images that exhibit smooth, continuous age transitions. This framework excels in capturing fine-grained aging details, particularly for elderly individuals. Quantitative and qualitative evaluations on benchmark datasets demonstrate the effectiveness of our approach in generating realistic age-progressed facial images, with a notable improvement in elderly aging accuracy and detail.