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。

人头完整的三维变形模型

Towards a complete 3D morphable model of the human head

3D face reconstruction from Internet photos has recently produced exciting
results. A person's face, e.g., Tom Hanks, can be modeled and animated in 3D
from a completely uncalibrated photo collection. Most methods, however, focus
solely on face area and mask out the rest of the head. This paper proposes that
head modeling from the Internet is a problem we can solve. We target
reconstruction of the rough shape of the head. Our method is to gradually
"grow" the head mesh starting from the frontal face and extending to the rest
of views using photometric stereo constraints. We call our method
boundary-value growing algorithm. Results on photos of celebrities downloaded
from the Internet are presented.

本文提出了一种称为边值增长算法的方法，通过光度立体约束从互联网上下载的名人照片中逐渐构建头部模型，以实现三维人脸重建，同时避免面部外胚层区域构建错误而导致的现有技术偏差。