Realistic Facial Age Transformation with 3D Uplifting

Abstract

While current facial re‐ageing methods can produce realistic results, they purely focus on the 2D age transformation. In this work, we present an approach to transform the age of a person in both facial appearance and shape across different ages while preserving their identity. We employ an α‐(de)blending diffusion network with an age‐to‐α transformation to generate coarse structure changes, such as wrinkles. Additionally, we edit biophysical skin properties, including melanin and hemoglobin, to simulate skin color changes, producing realistic re‐ageing results from ages 10 to 80 years. We also propose a geometric neural network that alters the coarse scale facial geometry according to age, followed by a lightweight and efficient network that adds appropriate skin displacement on top of the coarse geometry. Both qualitative and quantitative comparisons show that our method outperforms current state‐of‐the‐art approaches.