PERSE: Personalized 3D Generative Avatars from A Single Portrait
Authors: name: Hyunsoo Cha, name: Inhee Lee, name: Hanbyul Joo

We present PERSE, a method for building an animatable personalized generative
avatar from a reference portrait. Our avatar model enables facial attribute
editing in a continuous and disentangled latent space to control each facial
attribute, while preserving the individual's identity. To achieve this, our
method begins by synthesizing large-scale synthetic 2D video datasets, where
each video contains consistent changes in the facial expression and viewpoint,
combined with a variation in a specific facial attribute from the original
input. We propose a novel pipeline to produce high-quality, photorealistic 2D
videos with facial attribute editing. Leveraging this synthetic attribute
dataset, we present a personalized avatar creation method based on the 3D
Gaussian Splatting, learning a continuous and disentangled latent space for
intuitive facial attribute manipulation. To enforce smooth transitions in this
latent space, we introduce a latent space regularization technique by using
interpolated 2D faces as supervision. Compared to previous approaches, we
demonstrate that PERSE generates high-quality avatars with interpolated
attributes while preserving identity of reference person.