用文字生成可编辑的3D头发，实现发型与头部解耦。
StrandHead: Text to Hair-Disentangled 3D Head Avatars Using Human-Centric Priors
- 通过2D生成模型蒸馏+几何引导网格化，实现文本驱动的3D发丝生成。
- 在10个测试发型上达到最优，支持发丝级编辑和物理仿真。
- 无需大量成对数据，适合虚拟人、游戏与影视场景应用。
虽然发型体现个性，但现有头像生成方法因数据局限或表示纠缠，难以建模真实发丝。本文提出StrandHead，一种文本驱动的新方法，可生成3D发丝并实现头部与发型解耦。不依赖大规模头发-文本配对数据，而是通过蒸馏预训练于人体网格数据的2D生成模型，从文本提示中生成逼真发丝。为此，提出基于发丝几何的网格化方法，确保蒸馏目标梯度能有效传递至神经发丝表示。优化过程引入统计显著的发型特征进行正则化，防止发丝不合理漂移。所用2D/3D人体先验有助于生成与文本对齐且真实的3D发丝。大量实验表明，StrandHead在文本到发丝生成与解耦3D头像建模上达到当前最佳性能。生成的3D头发可用于头像的发丝级编辑，也可在图形引擎中实现物理模拟等应用。
原文摘要 · Abstract (English)
While haircut indicates distinct personality, existing avatar generation methods fail to model practical hair due to the data limitation or entangled representation. We propose StrandHead, a novel text-driven method capable of generating 3D hair strands and disentangled head avatars with strand-level attributes. Instead of using large-scale hair-text paired data for supervision, we demonstrate that realistic hair strands can be generated from prompts by distilling 2D generative models pre-trained on human mesh data. To this end, we propose a meshing approach guided by strand geometry to guarantee the gradient flow from the distillation objective to the neural strand representation. The optimization is then regularized by statistically significant haircut features, leading to stable updating of strands against unreasonable drifting. These employed 2D/3D human-centric priors contribute to text-aligned and realistic 3D strand generation. Extensive experiments show that StrandHead achieves the state-of-the-art performance on text to strand generation and disentangled 3D head avatar modeling. The generated 3D hair can be applied on avatars for strand-level editing, as well as implemented in the graphics engine for physical simulation or other applications. Project page: https://xiaokunsun.github.io/StrandHead.github.io/.
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。