PERSE: Personalized 3D Generative Avatars from A Single Portrait
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.
67735ba473932c3aa94fe0ac
Hyunsoo Cha
claimed_verified
2025-01-01T20:13:11.033Z
62414e2b585605a4079c2f38
/avatars/db1dc5dd2164b7ecbd789104329296bd.svg
Hyunsoo Cha
user
HyunsooCha
67735ba473932c3aa94fe0ad
Inhee Lee
67735ba473932c3aa94fe0ae
Hanbyul Joo
PERSE: Personalized 3D Generative Avatars from A Single Portrait
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.
PERSE generates animatable personalized avatars from portraits using 3D Gaussian Splatting to enable continuous and disentangled facial attribute editing.
animatable avatar personalized generative avatar facial attribute editing latent space disentangled latent space synthetic 2D video datasets photorealistic 2D videos facial expression changes viewpoint changes specific facial attribute 3D Gaussian Splatting latent space regularization interpolated 2D faces intuitive facial attribute manipulation