The use of deep learning to undertake shape analysis of the complexities of
the human head holds great promise. However, there have traditionally been a
number of barriers to accurate modelling, especially when operating on both a
global and local level. In this work, we will discuss the application of the
Swap Disentangled Variational Autoencoder (SD-VAE) with relevance to Crouzon,
Apert and Muenke syndromes. Although syndrome classification is performed on
the entire mesh, it is also possible, for the first time, to analyse the
influence of each region of the head on the syndromic phenotype. By
manipulating specific parameters of the generative model, and producing
procedure-specific new shapes, it is also possible to simulate the outcome of a
range of craniofacial surgical procedures. This opens new avenues to advance
diagnosis, aids surgical planning and allows for the objective evaluation of
surgical outcomes.

深度学习在人头部的形状分析方面具有巨大潜力，本文介绍了使用 Swap Disentangled Variational Autoencoder (SD-VAE) 对 Crouzon、Apert 和 Muenke 综合征进行分类和研究，同时第一次分析了头部各个区域对综合征表型的影响，并通过调整生成模型的特定参数和生成特定程序的新形状，模拟了一系列颅颜面手术的结果，从而为进一步的诊断、手术规划和手术效果的客观评估开辟了新的途径。

网格式变分自编码器中的潜在解缠有助于颅面综合征的诊断及手术规划

Latent Disentanglement in Mesh Variational Autoencoders Improves the  Diagnosis of Craniofacial Syndromes and Aids Surgical Planning

Acquisition and rendering of photo-realistic human heads is a highly
challenging research problem of particular importance for virtual telepresence.
Currently, the highest quality is achieved by volumetric approaches trained in
a person specific manner on multi-view data. These models better represent fine
structure, such as hair, compared to simpler mesh-based models. Volumetric
models typically employ a global code to represent facial expressions, such
that they can be driven by a small set of animation parameters. While such
architectures achieve impressive rendering quality, they can not easily be
extended to the multi-identity setting. In this paper, we devise a novel
approach for predicting volumetric avatars of the human head given just a small
number of inputs. We enable generalization across identities by a novel
parameterization that combines neural radiance fields with local, pixel-aligned
features extracted directly from the inputs, thus sidestepping the need for
very deep or complex networks. Our approach is trained in an end-to-end manner
solely based on a photometric re-rendering loss without requiring explicit 3D
supervision.We demonstrate that our approach outperforms the existing state of
the art in terms of quality and is able to generate faithful facial expressions
in a multi-identity setting.

本文提出一种利用神经放射场和局部像素对齐特征预测人头部的体积化代理，可以在多身份环境中进行泛化，并且无需明确的 3D 监督。该方法在以光度渲染损失为基础的端到端训练中表现出更好的质量，并且能够生成真实的面部表情。

PVA：像素对齐的容积化身

PVA: Pixel-aligned Volumetric Avatars

Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for
representing the 3D shapes and textures of an object class. Here we present the
most complete 3DMM of the human head to date that includes face, cranium, ears,
eyes, teeth and tongue. To achieve this, we propose two methods for combining
existing 3DMMs of different overlapping head parts: i. use a regressor to
complete missing parts of one model using the other, ii. use the Gaussian
Process framework to blend covariance matrices from multiple models. Thus we
build a new combined face-and-head shape model that blends the variability and
facial detail of an existing face model (the LSFM) with the full head modelling
capability of an existing head model (the LYHM). Then we construct and fuse a
highly-detailed ear model to extend the variation of the ear shape. Eye and eye
region models are incorporated into the head model, along with basic models of
the teeth, tongue and inner mouth cavity. The new model achieves
state-of-the-art performance. We use our model to reconstruct full head
representations from single, unconstrained images allowing us to parameterize
craniofacial shape and texture, along with the ear shape, eye gaze and eye
color.

本文提出一种新的 3D 人头模型，融合现有的人脸模型和头部模型并加入耳朵，眼睛，牙齿，舌头等特征的模型，实现了最新的复原效果，并可以从单张图片重现整个头部的表面特征参数。