Re-Aging GAN: Toward Personalized Face Age Transformation

Face age transformation aims to synthesize past or future face images by reflecting the age factor on given faces. Ideally, this task should synthesize natural-looking faces across various age groups while maintaining identity. However, most of the existing work has focused on only one of these or is difficult to train while unnatural artifacts still appear. In this work, we propose Re-Aging GAN (RAGAN), a novel single framework considering all the critical factors in age transformation. Our framework achieves state-of-the-art personalized face age transformation by compelling the input identity to perform the self-guidance of the generation process. Specifically, RAGAN can learn the personalized age features by using high-order interactions between given identity and target age. Learned personalized age features are identity information that is recalibrated according to the target age. Hence, such features encompass identity and target age information that provides important clues on how an input identity should be at a certain age. Experimental result shows the lowest FID and KID scores and the highest age recognition accuracy compared to previous methods. The proposed method also demonstrates the visual superiority with fewer artifacts, identity preservation, and natural transformation across various age groups.