HairGPT: Strand-as-Language Autoregressive Modeling for Realistic 3D Hairstyle Synthesis
Hair is a rich medium of visual and cultural expression, yet its digital modeling remains challenging due to the duality of fluidity and structure. Many existing generative approaches rely primarily on continuous diffusion fields, which entangle global topology with local texture and obscure the semantic and structural organization of hairstyles. To address this, we propose HairGPT, a strand-centric framework that treats strands as generative primitives and formulates realistic 3D hairstyle synthesis as a dual-decoupled autoregressive sequence modeling problem. Our method applies spatial decoupling across semantic scalp regions and structural decoupling along a hierarchical strand represe...
| Authors | Haimin Luo, Min Ouyang, Lan Xu et al. |
|---|---|
| Primary category | cs.GR |
| Published | 9 May 2026 |
| Last updated at source | 9 May 2026 |
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