StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity
  3D Avatar Generation
The recent advancements in image-text diffusion models have stimulated
research interest in large-scale 3D generative models. Nevertheless, the
limited availability of diverse 3D resources presents significant challenges to
learning. In this paper, we present a novel method for generating high-quality,
stylized 3D avatars that utilizes pre-trained image-text diffusion models for
data generation and a Generative Adversarial Network (GAN)-based 3D generation
network for training. Our method leverages the comprehensive priors of
appearance and geometry offered by image-text diffusion models to generate
multi-view images of avatars in various styles. During data generation, we
employ poses extracted from existing 3D models to guide the generation of
multi-view images. To address the misalignment between poses and images in
data, we investigate view-specific prompts and develop a coarse-to-fine
discriminator for GAN training. We also delve into attribute-related prompts to
increase the diversity of the generated avatars. Additionally, we develop a
latent diffusion model within the style space of StyleGAN to enable the
generation of avatars based on image inputs. Our approach demonstrates superior
performance over current state-of-the-art methods in terms of visual quality
and diversity of the produced avatars.
6476b8cc83d4fdaedddf7ecf
Chi Zhang
claimed_verified
2023-12-19T08:56:06.374Z
64196320ed725fef64419c2a
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Chi Zhang
user
DrChiZhang
6476b8cc83d4fdaedddf7ed0
Yiwen Chen
admin_assigned
2023-05-31T08:27:01.798Z
6422a9669a00fbe9dfe665a1
/avatars/288bf5ea18205bcb0bbea29a304ffbbb.svg
Yiwen Chen
user
NTU-yiwen
6476b8cc83d4fdaedddf7ed1
Yijun Fu
6476b8cc83d4fdaedddf7ed2
Zhenglin Zhou
admin_assigned
2023-05-31T08:29:10.291Z
6425318d175bd2952281065e
/avatars/37deb6ceb1552dece43a1c8c13c1c871.svg
ZhenglinZhou
user
zhenglin
6476b8cc83d4fdaedddf7ed3
Gang YU
claimed_verified
2023-06-01T05:54:23.427Z
63417332c5565a4b8d43a0d8
https://cdn-avatars.huggingface.co/v1/production/uploads/63417332c5565a4b8d43a0d8/MmnYG7Wu2Z_lqCBvTfJmy.png
Gang Yu
user
skicy
6476b8cc83d4fdaedddf7ed4
Billzb Wang
6476b8cc83d4fdaedddf7ed5
Bin Fu
6476b8cc83d4fdaedddf7ed6
Tao Chen
6476b8cc83d4fdaedddf7ed7
Guosheng Lin
6476b8cc83d4fdaedddf7ed8
Chunhua Shen
StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity
  3D Avatar Generation
The recent advancements in image-text diffusion models have stimulated
research interest in large-scale 3D generative models. Nevertheless, the
limited availability of diverse 3D resources presents significant challenges to
learning. In this paper, we present a novel method for generating high-quality,
stylized 3D avatars that utilizes pre-trained image-text diffusion models for
data generation and a Generative Adversarial Network (GAN)-based 3D generation
network for training. Our method leverages the comprehensive priors of
appearance and geometry offered by image-text diffusion models to generate
multi-view images of avatars in various styles. During data generation, we
employ poses extracted from existing 3D models to guide the generation of
multi-view images. To address the misalignment between poses and images in
data, we investigate view-specific prompts and develop a coarse-to-fine
discriminator for GAN training. We also delve into attribute-related prompts to
increase the diversity of the generated avatars. Additionally, we develop a
latent diffusion model within the style space of StyleGAN to enable the
generation of avatars based on image inputs. Our approach demonstrates superior
performance over current state-of-the-art methods in terms of visual quality
and diversity of the produced avatars.
A novel method generates high-quality, stylized 3D avatars using pre-trained image-text diffusion models and a GAN-based 3D generation network, addressing challenges in 3D data generation.
image-text diffusion models Generative Adversarial Network multi-view images view-specific prompts coarse-to-fine discriminator attribute-related prompts latent diffusion model StyleGAN