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Hair plays a significant role in personal identity and appearance, making itan essential component of high-quality, photorealistic avatars. Existingapproaches either focus on modeling the facial region only or rely onpersonalized models, limiting their generalizability and scalability. In thispaper, we present a novel method for creating high-fidelity avatars withdiverse hairstyles. Our method leverages the local similarity across differenthairstyles and learns a universal hair appearance prior from multi-viewcaptures of hundreds of people. This prior model takes 3D-aligned features asinput and generates dense radiance fields conditioned on a sparse point cloudwith color. As our model splits different hairstyles into local primitives andbuilds prior at that level, it is capable of handling various hair topologies.Through experiments, we demonstrate that our model captures a diverse range ofhairstyles and generalizes well to challenging new hairstyles. Empiricalresults show that our method improves the state-of-the-art approaches incapturing and generating photorealistic, personalized avatars with completehair.

A Local Appearance Model for Volumetric Capture of Diverse Hairstyle