Li, XiaohuiGuarnera, Giuseppe ClaudioLin, ArvinGhosh, AbhijeetGarces, ElenaHaines, Eric2024-06-252024-06-2520241467-8659https://doi.org/10.1111/cgf.15146https://diglib.eg.org/handle/10.1111/cgf15146While 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.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Texturing; Reflectance modeling; Shape modelingComputing methodologies → TexturingReflectance modelingShape modeling10.1111/cgf.1514612 pages