StrandHead: Text to Strand-Disentangled 3D Head Avatars Using Hair
  Geometric Priors
Authors: name: Xiaokun Sun, name: Zeyu Cai, name: Zhenyu Zhang, name: Ying Tai, name: Jian Yang

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.