title: StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity
    3D Avatar Generation
summary: 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.
paper_authors._id: 6476b8cc83d4fdaedddf7ecf
paper_authors.name: Chi Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-19T08:56:06.374Z
paper_authors._id: 64196320ed725fef64419c2a
paper_authors.avatarUrl: /avatars/96feb22fb5e8931d6c9e0ea06148266f.svg
paper_authors.fullname: Chi Zhang
paper_authors.type: user
paper_authors.user: DrChiZhang
paper_authors._id: 6476b8cc83d4fdaedddf7ed0
paper_authors.name: Yiwen Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-31T08:27:01.798Z
paper_authors._id: 6422a9669a00fbe9dfe665a1
paper_authors.avatarUrl: /avatars/288bf5ea18205bcb0bbea29a304ffbbb.svg
paper_authors.fullname: Yiwen Chen
paper_authors.type: user
paper_authors.user: NTU-yiwen
paper_authors._id: 6476b8cc83d4fdaedddf7ed1
paper_authors.name: Yijun Fu
paper_authors._id: 6476b8cc83d4fdaedddf7ed2
paper_authors.name: Zhenglin Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-31T08:29:10.291Z
paper_authors._id: 6425318d175bd2952281065e
paper_authors.avatarUrl: /avatars/37deb6ceb1552dece43a1c8c13c1c871.svg
paper_authors.fullname: ZhenglinZhou
paper_authors.type: user
paper_authors.user: zhenglin
paper_authors._id: 6476b8cc83d4fdaedddf7ed3
paper_authors.name: Gang YU
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-01T05:54:23.427Z
paper_authors._id: 63417332c5565a4b8d43a0d8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63417332c5565a4b8d43a0d8/MmnYG7Wu2Z_lqCBvTfJmy.png
paper_authors.fullname: Gang Yu
paper_authors.type: user
paper_authors.user: skicy
paper_authors._id: 6476b8cc83d4fdaedddf7ed4
paper_authors.name: Billzb Wang
paper_authors._id: 6476b8cc83d4fdaedddf7ed5
paper_authors.name: Bin Fu
paper_authors._id: 6476b8cc83d4fdaedddf7ed6
paper_authors.name: Tao Chen
paper_authors._id: 6476b8cc83d4fdaedddf7ed7
paper_authors.name: Guosheng Lin
paper_authors._id: 6476b8cc83d4fdaedddf7ed8
paper_authors.name: Chunhua Shen
paper_title: StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity
    3D Avatar Generation
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: image-text diffusion models Generative Adversarial Network multi-view images view-specific prompts coarse-to-fine discriminator attribute-related prompts latent diffusion model StyleGAN