HairGPT: 基于发丝的语言自回归建模用于逼真3D发型合成
HairGPT: Strand-as-Language Autoregressive Modeling for Realistic 3D Hairstyle Synthesis
AI总结：
HairGPT通过发丝为中心的框架，将发型合成转化为双解耦的自回归序列建模问题，实现结构和语义的解耦，支持复杂发型合成与风格化领域适应。
摘要：
发丝是视觉和文化表达的丰富介质，但其数字建模仍具挑战性，因涉及流体性和结构的双重性。现有生成方法主要依赖连续扩散场，将全局拓扑与局部纹理纠缠，模糊了发型的语义和结构组织。为此，我们提出HairGPT，一个以发丝为中心的框架，将发丝视为生成原语，将逼真3D发型合成视为双解耦的自回归序列建模问题。我们的方法在语义头皮区域进行空间解耦，并在层次化发丝表示上进行结构解耦，从全局布局到细粒度风格逐步推进。我们进一步引入几何标记器和区域感知的语义标注，指导发丝级生成，实现组合编辑、稀有复杂发型合成及风格化领域适应。通过将生成建模与数字梳理工作流程对齐，HairGPT将发生成为从模糊纹理合成到结构化和语义可控的创作过程，支持在现实和风格化领域中实现稳健的语义条件和高保真结果。项目页面：https://haiminluo.github.io/hairgpt/
英文摘要：
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 representation, progressing from global layout to fine-grained style. We further introduce a geometric tokenizer and region-aware semantic annotations to guide strand-level generation, enabling compositional editing, synthesis of rare and complex hairstyles, and adaptation to stylized domains. By aligning generative modeling with the workflow of digital grooming, HairGPT turns hair generation from opaque texture synthesis into a structured and semantically controllable authoring process, supporting robust semantic conditioning and high-fidelity results across realistic and stylized domains. Project Page: https://haiminluo.github.io/hairgpt/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
