43-Issue 4

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Realistic Facial Age Transformation with 3D Uplifting
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Li, Xiaohui; Guarnera, Giuseppe Claudio; Lin, Arvin; Ghosh, Abhijeet; Garces, Elena; Haines, Eric
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.