Latent age attribute modulation guided continuous aging facial image generation

In this paper, we propose an innovative framework to create the Latent Age Attribute Module (LAAM), which maps age attribute variables to a prior latent space that is easy to sample. By continuously sampling the high-dimensional age space, we accurately characterize the distribution of age attribute variables. Additionally, we introduce the Age-AdaIN Fusion Module (AFM), which fully integrates the age attributes features mapped from LAAM with the content features of the face, generating images that transition smoothly and continuously across different age. Furthermore, due to the advantage of the continuous characterization of the latent age attribute, the proposed method could better capture and generate fine-grained aging details especially for elderly individuals. Through quantitative and qualitative analyses on existing datasets, we validate the effectiveness of our proposed method in face aging and the performance enhancement especially with respect to the elderly aging facial image generation.