title: AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation
summary: We introduce AvatarBooth, a novel method for generating high-quality 3D
  avatars using text prompts or specific images. Unlike previous approaches that
  can only synthesize avatars based on simple text descriptions, our method
  enables the creation of personalized avatars from casually captured face or
  body images, while still supporting text-based model generation and editing.
  Our key contribution is the precise avatar generation control by using dual
  fine-tuned diffusion models separately for the human face and body. This
  enables us to capture intricate details of facial appearance, clothing, and
  accessories, resulting in highly realistic avatar generations. Furthermore, we
  introduce pose-consistent constraint to the optimization process to enhance the
  multi-view consistency of synthesized head images from the diffusion model and
  thus eliminate interference from uncontrolled human poses. In addition, we
  present a multi-resolution rendering strategy that facilitates coarse-to-fine
  supervision of 3D avatar generation, thereby enhancing the performance of the
  proposed system. The resulting avatar model can be further edited using
  additional text descriptions and driven by motion sequences. Experiments show
  that AvatarBooth outperforms previous text-to-3D methods in terms of rendering
  and geometric quality from either text prompts or specific images. Please check
  our project website at https://zeng-yifei.github.io/avatarbooth_page/.
paper_authors._id: 648fd3f860488151a13cae54
paper_authors.name: Yifei Zeng
paper_authors._id: 648fd3f860488151a13cae55
paper_authors.name: Yuanxun Lu
paper_authors._id: 648fd3f860488151a13cae56
paper_authors.name: Xinya Ji
paper_authors._id: 648fd3f860488151a13cae57
paper_authors.name: Yao Yao
paper_authors._id: 648fd3f860488151a13cae58
paper_authors.name: Hao Zhu
paper_authors._id: 648fd3f860488151a13cae59
paper_authors.name: Xun Cao
paper_title: AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation
paper_summary: We introduce AvatarBooth, a novel method for generating high-quality 3D
  avatars using text prompts or specific images. Unlike previous approaches that
  can only synthesize avatars based on simple text descriptions, our method
  enables the creation of personalized avatars from casually captured face or
  body images, while still supporting text-based model generation and editing.
  Our key contribution is the precise avatar generation control by using dual
  fine-tuned diffusion models separately for the human face and body. This
  enables us to capture intricate details of facial appearance, clothing, and
  accessories, resulting in highly realistic avatar generations. Furthermore, we
  introduce pose-consistent constraint to the optimization process to enhance the
  multi-view consistency of synthesized head images from the diffusion model and
  thus eliminate interference from uncontrolled human poses. In addition, we
  present a multi-resolution rendering strategy that facilitates coarse-to-fine
  supervision of 3D avatar generation, thereby enhancing the performance of the
  proposed system. The resulting avatar model can be further edited using
  additional text descriptions and driven by motion sequences. Experiments show
  that AvatarBooth outperforms previous text-to-3D methods in terms of rendering
  and geometric quality from either text prompts or specific images. Please check
  our project website at https://zeng-yifei.github.io/avatarbooth_page/.
paper_ai_summary: AvatarBooth generates high-quality 3D avatars from text prompts or images using dual fine-tuned diffusion models and a multi-resolution rendering strategy, achieving superior rendering and geometric quality.
paper_ai_keywords: diffusion models pose-consistent constraint multi-resolution rendering strategy 3D avatar generation