用多视角图像生成任意风格和视角的3D头发，支持复杂发型还原。
TANGLED: Generating 3D Hair Strands from Images with Arbitrary Styles and Viewpoints
- 基于多视图线稿的扩散模型捕捉发丝密度与分界线等拓扑特征
- 在457种发型数据集上实现跨风格、跨视角的稳定生成
- 适合数字人、动画与AR应用，尤其擅长编织类复杂发型
发型结构复杂且具有文化意义，现有文本或图像引导生成方法难以处理多样风格。本文提出TANGLED，一种支持多种输入条件的3D发丝生成方法。首先构建包含457种发型、标注74个属性的MultiHair数据集，强调复杂与文化代表性发型以提升泛化能力；其次设计基于多视图线稿的扩散框架，通过潜在扩散模型结合线稿特征的交叉注意力，有效捕捉发丝密度、分界线等拓扑信息并过滤噪声；最后引入参数化后处理模块，对辫状结构施加约束以保持复杂结构连贯性。该框架不仅提升发型真实感与多样性，还支持文化包容性数字人构建，并可用于动画与增强现实中的草图驱动3D发丝编辑。
原文摘要 · Abstract (English)
Hairstyles are intricate and culturally significant with various geometries, textures, and structures. Existing text or image-guided generation methods fail to handle the richness and complexity of diverse styles. We present TANGLED, a novel approach for 3D hair strand generation that accommodates diverse image inputs across styles, viewpoints, and quantities of input views. TANGLED employs a three-step pipeline. First, our MultiHair Dataset provides 457 diverse hairstyles annotated with 74 attributes, emphasizing complex and culturally significant styles to improve model generalization. Second, we propose a diffusion framework conditioned on multi-view linearts that can capture topological cues (e.g., strand density and parting lines) while filtering out noise. By leveraging a latent diffusion model with cross-attention on lineart features, our method achieves flexible and robust 3D hair generation across diverse input conditions. Third, a parametric post-processing module enforces braid-specific constraints to maintain coherence in complex structures. This framework not only advances hairstyle realism and diversity but also enables culturally inclusive digital avatars and novel applications like sketch-based 3D strand editing for animation and augmented reality.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。