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