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.

本文介绍一种创建高度逼真、个性化发型的头像的新方法，通过利用不同发型间的局部相似性并从多视角捕捉数百人的数据生成通用的发型外观先验模型，该先验模型以 3D 对齐特征为输入，在稀疏点云颜色条件下生成密集辐射场，通过实验证明该模型能够捕捉各种多样的发型并且应用在生成逼真、个性化的具有完整发型的头像上，取得了优于现有方法的效果。

多样发型体积捕捉的本地外观模型

A Local Appearance Model for Volumetric Capture of Diverse Hairstyle

It is broadly accepted that there is a "gender gap" in face recognition
accuracy, with females having higher false match and false non-match rates.
However, relatively little is known about the cause(s) of this gender gap. Even
the recent NIST report on demographic effects lists "analyze cause and effect"
under "what we did not do". We first demonstrate that female and male
hairstyles have important differences that impact face recognition accuracy. In
particular, compared to females, male facial hair contributes to creating a
greater average difference in appearance between different male faces. We then
demonstrate that when the data used to estimate recognition accuracy is
balanced across gender for how hairstyles occlude the face, the initially
observed gender gap in accuracy largely disappears. We show this result for two
different matchers, and analyzing images of Caucasians and of
African-Americans. These results suggest that future research on demographic
variation in accuracy should include a check for balanced quality of the test
data as part of the problem formulation. To promote reproducible research,
matchers, attribute classifiers, and datasets used in this research are/will be
publicly available.

通过实验发现，男女发型有重要差异，尤其是男性胡须会造成不同男性面孔之间的平均外观差异更大，而当基于发型来遮挡面部的数据在性别上平衡后，初始的性别差距在识别准确性上大部分消失，这个结果对未来研究中包含性别差异具有启示意义。