title: AvatarArtist: Open-Domain 4D Avatarization
summary: This work focuses on open-domain 4D avatarization, with the purpose of
  creating a 4D avatar from a portrait image in an arbitrary style. We select
  parametric triplanes as the intermediate 4D representation and propose a
  practical training paradigm that takes advantage of both generative adversarial
  networks (GANs) and diffusion models. Our design stems from the observation
  that 4D GANs excel at bridging images and triplanes without supervision yet
  usually face challenges in handling diverse data distributions. A robust 2D
  diffusion prior emerges as the solution, assisting the GAN in transferring its
  expertise across various domains. The synergy between these experts permits the
  construction of a multi-domain image-triplane dataset, which drives the
  development of a general 4D avatar creator. Extensive experiments suggest that
  our model, AvatarArtist, is capable of producing high-quality 4D avatars with
  strong robustness to various source image domains. The code, the data, and the
  models will be made publicly available to facilitate future studies..
paper_authors._id: 67e3c2afc12b767434674a01
paper_authors.name: Hongyu Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-26T20:44:14.206Z
paper_authors._id: 640d96783c82bd463ee1fe6b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Ct2OPR83Vt6VTrzpu79BE.png
paper_authors.fullname: Hongyu LIU
paper_authors.type: user
paper_authors.user: KumaPower
paper_authors._id: 67e3c2afc12b767434674a02
paper_authors.name: Xuan Wang
paper_authors._id: 67e3c2afc12b767434674a03
paper_authors.name: Ziyu Wan
paper_authors._id: 67e3c2afc12b767434674a04
paper_authors.name: Yue Ma
paper_authors._id: 67e3c2afc12b767434674a05
paper_authors.name: Jingye Chen
paper_authors._id: 67e3c2afc12b767434674a06
paper_authors.name: Yanbo Fan
paper_authors._id: 67e3c2afc12b767434674a07
paper_authors.name: Yujun Shen
paper_authors._id: 67e3c2afc12b767434674a08
paper_authors.name: Yibing Song
paper_authors._id: 67e3c2afc12b767434674a09
paper_authors.name: Qifeng Chen
paper_title: AvatarArtist: Open-Domain 4D Avatarization
paper_summary: This work focuses on open-domain 4D avatarization, with the purpose of
  creating a 4D avatar from a portrait image in an arbitrary style. We select
  parametric triplanes as the intermediate 4D representation and propose a
  practical training paradigm that takes advantage of both generative adversarial
  networks (GANs) and diffusion models. Our design stems from the observation
  that 4D GANs excel at bridging images and triplanes without supervision yet
  usually face challenges in handling diverse data distributions. A robust 2D
  diffusion prior emerges as the solution, assisting the GAN in transferring its
  expertise across various domains. The synergy between these experts permits the
  construction of a multi-domain image-triplane dataset, which drives the
  development of a general 4D avatar creator. Extensive experiments suggest that
  our model, AvatarArtist, is capable of producing high-quality 4D avatars with
  strong robustness to various source image domains. The code, the data, and the
  models will be made publicly available to facilitate future studies..
paper_ai_summary: The integration of parametric triplanes with GANs and diffusion models enables the creation of high-quality, style-agnostic 4D avatars from portraits across diverse domains.
paper_ai_keywords: parametric triplanes generative adversarial networks 4D GANs diffusion models 2D diffusion prior multi-domain image-triplane dataset AvatarArtist