AvatarArtist: Open-Domain 4D Avatarization
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..
67e3c2afc12b767434674a01
Hongyu Liu
claimed_verified
2025-03-26T20:44:14.206Z
640d96783c82bd463ee1fe6b
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Ct2OPR83Vt6VTrzpu79BE.png
Hongyu LIU
user
KumaPower
67e3c2afc12b767434674a02
Xuan Wang
67e3c2afc12b767434674a03
Ziyu Wan
67e3c2afc12b767434674a04
Yue Ma
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Jingye Chen
67e3c2afc12b767434674a06
Yanbo Fan
67e3c2afc12b767434674a07
Yujun Shen
67e3c2afc12b767434674a08
Yibing Song
67e3c2afc12b767434674a09
Qifeng Chen
AvatarArtist: Open-Domain 4D Avatarization
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..
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.
parametric triplanes generative adversarial networks 4D GANs diffusion models 2D diffusion prior multi-domain image-triplane dataset AvatarArtist