A Local Appearance Model for Volumetric Capture of Diverse Hairstyle

Abstract

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.