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

Few-shot classification aims at classifying categories of a novel task by
learning from just a few (typically, 1 to 5) labelled examples. An effective
approach to few-shot classification involves a prior model trained on a
large-sample base domain, which is then finetuned over the novel few-shot task
to yield generalizable representations. However, task-specific finetuning is
prone to overfitting due to the lack of enough training examples. To alleviate
this issue, we propose a new finetuning approach based on contrastive learning
that reuses unlabelled examples from the base domain in the form of
distractors. Unlike the nature of unlabelled data used in prior works,
distractors belong to classes that do not overlap with the novel categories. We
demonstrate for the first time that inclusion of such distractors can
significantly boost few-shot generalization. Our technical novelty includes a
stochastic pairing of examples sharing the same category in the few-shot task
and a weighting term that controls the relative influence of task-specific
negatives and distractors. An important aspect of our finetuning objective is
that it is agnostic to distractor labels and hence applicable to various base
domain settings. Compared to state-of-the-art approaches, our method shows
accuracy gains of up to $12\%$ in cross-domain and up to $5\%$ in unsupervised
prior-learning settings.

本研究提出了一种基于对比学习的微调方法，使用来自底层域的未标记示例作为干扰项，在几种少样本分类任务中可以明显提高泛化性能。

论少样本分类中诱导误导因素的重要性

On the Importance of Distractors for Few-Shot Classification

Markov random fields (MRFs) have been widely used as prior models in various
inverse problems such as tomographic reconstruction. While MRFs provide a
simple and often effective way to model the spatial dependencies in images,
they suffer from the fact that parameter estimation is difficult. In practice,
this means that MRFs typically have very simple structure that cannot
completely capture the subtle characteristics of complex images.
In this paper, we present a novel Gaussian mixture Markov random field model
(GM-MRF) that can be used as a very expressive prior model for inverse problems
such as denoising and reconstruction. The GM-MRF forms a global image model by
merging together individual Gaussian-mixture models (GMMs) for image patches.
In addition, we present a novel analytical framework for computing MAP
estimates using the GM-MRF prior model through the construction of surrogate
functions that result in a sequence of quadratic optimizations. We also
introduce a simple but effective method to adjust the GM-MRF so as to control
the sharpness in low- and high-contrast regions of the reconstruction
separately. We demonstrate the value of the model with experiments including
image denoising and low-dose CT reconstruction.

本文介绍了一种高效的先验模型 —— 高斯混合 Markov 随机场模型 (GM-MRF)，以及通过构建替代函数的新型分析框架，在低对比度和高对比度区域分别控制图像重建锐度的方法。实验证明了该模型在图像去噪和低剂量 CT 重建方面的价值。