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_summary: PERSE generates animatable personalized avatars from portraits using 3D Gaussian Splatting to enable continuous and disentangled facial attribute editing.
paper_ai_keywords: 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