title: PERSE: Personalized 3D Generative Avatars from A Single Portrait
summary: 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.
paper_authors._id: 67735ba473932c3aa94fe0ac
paper_authors.name: Hyunsoo Cha
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-01T20:13:11.033Z
paper_authors._id: 62414e2b585605a4079c2f38
paper_authors.avatarUrl: /avatars/db1dc5dd2164b7ecbd789104329296bd.svg
paper_authors.fullname: Hyunsoo Cha
paper_authors.type: user
paper_authors.user: HyunsooCha
paper_authors._id: 67735ba473932c3aa94fe0ad
paper_authors.name: Inhee Lee
paper_authors._id: 67735ba473932c3aa94fe0ae
paper_authors.name: Hanbyul Joo
paper_title: PERSE: Personalized 3D Generative Avatars from A Single Portrait
paper_summary: 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.
paper_ai_keywords: animatable personalized generative avatar facial attribute editing continuous and disentangled latent space synthetic 2D video datasets facial expression viewpoint 3D Gaussian Splatting latent space regularization technique interpolated 2D faces