title: SEEAvatar: Photorealistic Text-to-3D Avatar Generation with Constrained
    Geometry and Appearance
summary: Powered by large-scale text-to-image generation models, text-to-3D avatar
  generation has made promising progress. However, most methods fail to produce
  photorealistic results, limited by imprecise geometry and low-quality
  appearance. Towards more practical avatar generation, we present SEEAvatar, a
  method for generating photorealistic 3D avatars from text with SElf-Evolving
  constraints for decoupled geometry and appearance. For geometry, we propose to
  constrain the optimized avatar in a decent global shape with a template avatar.
  The template avatar is initialized with human prior and can be updated by the
  optimized avatar periodically as an evolving template, which enables more
  flexible shape generation. Besides, the geometry is also constrained by the
  static human prior in local parts like face and hands to maintain the delicate
  structures. For appearance generation, we use diffusion model enhanced by
  prompt engineering to guide a physically based rendering pipeline to generate
  realistic textures. The lightness constraint is applied on the albedo texture
  to suppress incorrect lighting effect. Experiments show that our method
  outperforms previous methods on both global and local geometry and appearance
  quality by a large margin. Since our method can produce high-quality meshes and
  textures, such assets can be directly applied in classic graphics pipeline for
  realistic rendering under any lighting condition. Project page at:
  https://seeavatar3d.github.io.
paper_authors._id: 657bd22264dab2f4c9fbfb55
paper_authors.name: Yuanyou Xu
paper_authors._id: 657bd22264dab2f4c9fbfb56
paper_authors.name: Zongxin Yang
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2023-12-15T04:12:19.909Z
paper_authors._id: 619bf9b3cbedb87e1a92fb3b
paper_authors.avatarUrl: /avatars/ee280db0232e21416c948ab9a9a2344e.svg
paper_authors.fullname: Zongxin Yang
paper_authors.type: user
paper_authors.user: z-x-yang
paper_authors._id: 657bd22264dab2f4c9fbfb57
paper_authors.name: Yi Yang
paper_title: SEEAvatar: Photorealistic Text-to-3D Avatar Generation with Constrained
    Geometry and Appearance
paper_summary: Powered by large-scale text-to-image generation models, text-to-3D avatar
  generation has made promising progress. However, most methods fail to produce
  photorealistic results, limited by imprecise geometry and low-quality
  appearance. Towards more practical avatar generation, we present SEEAvatar, a
  method for generating photorealistic 3D avatars from text with SElf-Evolving
  constraints for decoupled geometry and appearance. For geometry, we propose to
  constrain the optimized avatar in a decent global shape with a template avatar.
  The template avatar is initialized with human prior and can be updated by the
  optimized avatar periodically as an evolving template, which enables more
  flexible shape generation. Besides, the geometry is also constrained by the
  static human prior in local parts like face and hands to maintain the delicate
  structures. For appearance generation, we use diffusion model enhanced by
  prompt engineering to guide a physically based rendering pipeline to generate
  realistic textures. The lightness constraint is applied on the albedo texture
  to suppress incorrect lighting effect. Experiments show that our method
  outperforms previous methods on both global and local geometry and appearance
  quality by a large margin. Since our method can produce high-quality meshes and
  textures, such assets can be directly applied in classic graphics pipeline for
  realistic rendering under any lighting condition. Project page at:
  https://seeavatar3d.github.io.
paper_ai_summary: SEEAvatar generates photorealistic 3D avatars from text using a self-evolving template for geometry and a diffusion model-enhanced rendering pipeline for appearance.
paper_ai_keywords: SEEAvatar SElf-Evolving constraints template avatar human prior physically based rendering pipeline diffusion model prompt engineering albedo texture lightness constraint