Face Aging via Diffusion-based Editing
Xiangyi Chen Stéphane Lathuilière
In this paper, we address the problem of face aging: generating past or
future facial images by incorporating age-related changes to the given face.
Previous aging methods rely solely on human facial image datasets and are thus
constrained by their inherent scale and bias. This restricts their application
to a limited generatable age range and the inability to handle large age gaps.
We propose FADING, a novel approach to address Face Aging via DIffusion-based
editiNG. We go beyond existing methods by leveraging the rich prior of
large-scale language-image diffusion models. First, we specialize a pre-trained
diffusion model for the task of face age editing by using an age-aware
fine-tuning scheme. Next, we invert the input image to latent noise and obtain
optimized null text embeddings. Finally, we perform text-guided local age
editing via attention control. The quantitative and qualitative analyses
demonstrate that our method outperforms existing approaches with respect to
aging accuracy, attribute preservation, and aging quality.