title: StrandHead: Text to Strand-Disentangled 3D Head Avatars Using Hair
    Geometric Priors
summary: While haircut indicates distinct personality, existing avatar generation
  methods fail to model practical hair due to the general or entangled
  representation. We propose StrandHead, a novel text to 3D head avatar
  generation method capable of generating disentangled 3D hair with strand
  representation. Without using 3D data for supervision, we demonstrate that
  realistic hair strands can be generated from prompts by distilling 2D
  generative diffusion models. To this end, we propose a series of reliable
  priors on shape initialization, geometric primitives, and statistical haircut
  features, leading to a stable optimization and text-aligned performance.
  Extensive experiments show that StrandHead achieves the state-of-the-art
  reality and diversity of generated 3D head and hair. The generated 3D hair can
  also be easily implemented in the Unreal Engine for physical simulation and
  other applications. The code will be available at
  https://xiaokunsun.github.io/StrandHead.github.io.
paper_authors._id: 6760edcb125981709b9f2e00
paper_authors.name: Xiaokun Sun
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-17T08:03:50.594Z
paper_authors._id: 65813fbeabafd960c84fdf2f
paper_authors.avatarUrl: /avatars/d8f9cb56a2e44c3ca4472099437e0b50.svg
paper_authors.fullname: Xiaokun Sun
paper_authors.type: user
paper_authors.user: XiaokunSun
paper_authors._id: 6760edcb125981709b9f2e01
paper_authors.name: Zeyu Cai
paper_authors._id: 6760edcb125981709b9f2e02
paper_authors.name: Zhenyu Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-17T11:15:41.589Z
paper_authors._id: 642e6cc9be01b88c9446fe3d
paper_authors.avatarUrl: /avatars/43a8f9bf35bea54bcda547174d921aa4.svg
paper_authors.fullname: Zhenyu Zhang
paper_authors.type: user
paper_authors.user: JesseZhang
paper_authors._id: 6760edcb125981709b9f2e03
paper_authors.name: Ying Tai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-17T08:03:48.616Z
paper_authors._id: 65734004769f3ee9bde1af10
paper_authors.avatarUrl: /avatars/d6310ed861972fd691687d8f47413f33.svg
paper_authors.fullname: Ying Tai
paper_authors.type: user
paper_authors.user: yingtai
paper_authors._id: 6760edcb125981709b9f2e04
paper_authors.name: Jian Yang
paper_title: StrandHead: Text to Strand-Disentangled 3D Head Avatars Using Hair
    Geometric Priors
paper_summary: While haircut indicates distinct personality, existing avatar generation
  methods fail to model practical hair due to the general or entangled
  representation. We propose StrandHead, a novel text to 3D head avatar
  generation method capable of generating disentangled 3D hair with strand
  representation. Without using 3D data for supervision, we demonstrate that
  realistic hair strands can be generated from prompts by distilling 2D
  generative diffusion models. To this end, we propose a series of reliable
  priors on shape initialization, geometric primitives, and statistical haircut
  features, leading to a stable optimization and text-aligned performance.
  Extensive experiments show that StrandHead achieves the state-of-the-art
  reality and diversity of generated 3D head and hair. The generated 3D hair can
  also be easily implemented in the Unreal Engine for physical simulation and
  other applications. The code will be available at
  https://xiaokunsun.github.io/StrandHead.github.io.
paper_ai_summary: StrandHead generates realistic 3D hair for avatars using 2D diffusion models with text-based prompts, achieving high quality and diversity.
paper_ai_keywords: 3D head avatar generation StrandHead 3D hair strand representation 2D generative diffusion models shape initialization geometric primitives statistical haircut features Unreal Engine