Traditional 3D morphable face models (3DMMs) provide fine-grained control
over expression but cannot easily capture geometric and appearance details.
Neural volumetric representations approach photorealism but are hard to animate
and do not generalize well to unseen expressions. To tackle this problem, we
propose IMavatar (Implicit Morphable avatar), a novel method for learning
implicit head avatars from monocular videos. Inspired by the fine-grained
control mechanisms afforded by conventional 3DMMs, we represent the expression-
and pose- related deformations via learned blendshapes and skinning fields.
These attributes are pose-independent and can be used to morph the canonical
geometry and texture fields given novel expression and pose parameters. We
employ ray marching and iterative root-finding to locate the canonical surface
intersection for each pixel. A key contribution is our novel analytical
gradient formulation that enables end-to-end training of IMavatars from videos.
We show quantitatively and qualitatively that our method improves geometry and
covers a more complete expression space compared to state-of-the-art methods.

提出了一种名为 IMavatar 的新方法，可以从单目视频中生成隐式头像。通过学习混合形状和蒙皮场来表示表情和姿势相关的变形，使得这些属性可以用于完成新表情和姿态下的几何和纹理场的变形，并通过光线行进和迭代根查找，为每个像素定位规范表面交点，从而实现了头像的生成，并通过端到端的训练，得出了比现有技术更完整的表情空间和几何形状。

从视频中生成的隐式可塑头像

I M Avatar: Implicit Morphable Head Avatars from Videos

In this paper, we provide a detailed survey of 3D Morphable Face Models over
the 20 years since they were first proposed. The challenges in building and
applying these models, namely capture, modeling, image formation, and image
analysis, are still active research topics, and we review the state-of-the-art
in each of these areas. We also look ahead, identifying unsolved challenges,
proposing directions for future research and highlighting the broad range of
current and future applications.

本文综述了自 3D 可塑人脸模型首次提出后 20 年来的研究现状，重点探讨模型采集、建模、图像生成和图像分析等方面的挑战和现有解决方案，同时回顾了当前和未来应用领域以及提出未解决的问题和未来研究方向。

3D 可塑面部模型 —— 过去，现在和未来

3D Morphable Face Models -- Past, Present and Future

In this paper, we present a novel open-source pipeline for face registration
based on Gaussian processes as well as an application to face image analysis.
Non-rigid registration of faces is significant for many applications in
computer vision, such as the construction of 3D Morphable face models (3DMMs).
Gaussian Process Morphable Models (GPMMs) unify a variety of non-rigid
deformation models with B-splines and PCA models as examples. GPMM separate
problem specific requirements from the registration algorithm by incorporating
domain-specific adaptions as a prior model. The novelties of this paper are the
following: (i) We present a strategy and modeling technique for face
registration that considers symmetry, multi-scale and spatially-varying
details. The registration is applied to neutral faces and facial expressions.
(ii) We release an open-source software framework for registration and
model-building, demonstrated on the publicly available BU3D-FE database. The
released pipeline also contains an implementation of an Analysis-by-Synthesis
model adaption of 2D face images, tested on the Multi-PIE and LFW database.
This enables the community to reproduce, evaluate and compare the individual
steps of registration to model-building and 3D/2D model fitting. (iii) Along
with the framework release, we publish a new version of the Basel Face Model
(BFM-2017) with an improved age distribution and an additional facial
expression model.

本文提出了一种基于高斯过程的新型开源面部配准管道以及其对面部图像分析的应用。通过引入面部对称性、多尺度和空间变化细节，该配准策略和建模技术可以应用于中性面部和面部表情，帮助构建三维可塑面部模型。同时，还发布了 BFM-2017 的新版本，其中包括更好的年龄分布和额外的面部表情模型，其开源软件框架及其实现的 AU 分析与综合模型适应（Analysis-by-Synthesis model adaption）已在多 PIE 和 LFW 数据库上进行了测试。