Hair plays a significant role in personal identity and appearance, making it an essential component of high-quality, photorealistic avatars. Existing approaches either focus on modeling the facial region only or rely on personalized models, limiting their generalizability and scalability. In this paper, we present a novel method for creating high-fidelity avatars with diverse hairstyles. Our method leverages the local similarity across different hairstyles and learns a universal hair appearance prior from multi-view captures of hundreds of people. This prior model takes 3D-aligned features as input and generates dense radiance fields conditioned on a sparse point cloud with color. As our model splits different hairstyles into local primitives and builds prior at that level, it is capable of handling various hair topologies. Through experiments, we demonstrate that our model captures a diverse range of hairstyles and generalizes well to challenging new hairstyles. Empirical results show that our method improves the state-of-the-art approaches in capturing and generating photorealistic, personalized avatars with complete hair.

翻译：头发在个人身份和外观中扮演着重要角色，因此成为高质量、逼真虚拟化身的关键组成部分。现有方法要么仅关注面部区域的建模，要么依赖于个性化模型，从而限制了其通用性和可扩展性。在本文中，我们提出了一种新颖的方法，用于创建具有多样化发型的高保真虚拟化身。我们的方法利用不同发型之间的局部相似性，并从数百人的多视角捕获中学习通用的头发外观先验。该先验模型以三维对齐特征作为输入，基于带有颜色的稀疏点云生成密集辐射场。由于我们的模型将不同发型拆分为局部基元并在该层级构建先验，因此能够处理各种发型拓扑结构。通过实验，我们证明该模型能够捕获多样化的发型，并对具有挑战性的新发型展现出良好的泛化能力。实证结果表明，我们的方法在捕获和生成带有完整头发的逼真个性化虚拟化身方面，优于现有最先进的技术。