一种用于多样发型体积捕获的局部外观模型
A Local Appearance Model for Volumetric Capture of Diverse Hairstyle
AI总结：
针对现有虚拟化身头发建模泛化性差的问题，提出基于局部基元的通用头发外观先验模型，可处理多样发型拓扑，在捕获生成带完整头发的高保真化身方面性能优于现有最优方法。
摘要：
头发在个人身份与外观中扮演重要角色，是高质量、具有照片真实感的虚拟化身的关键组成部分。现有方法要么仅聚焦于面部区域建模，要么依赖个性化模型，限制了其泛化性与可扩展性。本文提出一种构建具有多样发型的高保真虚拟化身的新方法。该方法利用不同发型间的局部相似性，从数百人的多视图捕获数据中学习通用头发外观先验。该先验模型以3D对齐特征为输入，基于带颜色的稀疏点云生成密集辐射场。由于模型将不同发型拆分为局部基元并在该层级构建先验，因此能够处理各种头发拓扑结构。实验证明，该模型可捕获多样发型，且能很好地泛化到具有挑战性的新发型上。实证结果表明，在捕获和生成具有完整头发的照片级真实感个性化虚拟化身方面，本文方法优于现有最优方法。
英文摘要：
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.
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。
