Current advances in human head modeling allow to generate plausible-looking
3D head models via neural representations. Nevertheless, constructing complete
high-fidelity head models with explicitly controlled animation remains an
issue. Furthermore, completing the head geometry based on a partial
observation, e.g. coming from a depth sensor, while preserving details is often
problematic for the existing methods. We introduce a generative model for
detailed 3D head meshes on top of an articulated 3DMM which allows explicit
animation and high-detail preservation at the same time. Our method is trained
in two stages. First, we register a parametric head model with vertex
displacements to each mesh of the recently introduced NPHM dataset of accurate
3D head scans. The estimated displacements are baked into a hand-crafted UV
layout. Second, we train a StyleGAN model in order to generalize over the UV
maps of displacements. The decomposition of the parametric model and
high-quality vertex displacements allows us to animate the model and modify it
semantically. We demonstrate the results of unconditional generation and
fitting to the full or partial observation. The project page is available at
this https URL

通过神经表征，当前人类头部建模的进展允许生成逼真的 3D 头部模型。然而，构建完整的高保真度头部模型，并在明确控制动画的同时保留细节仍然是一个问题。我们引入了一种生成模型，用于在可显式动画和高细节保存之间创建详细的 3D 头部网格。

HeadCraft: 建模用于动态三维面模型的高细节形状变化

HeadCraft: Modeling High-Detail Shape Variations for Animated 3DMMs

Recently, the reconstruction of high-fidelity 3D head models from static
portrait image has made great progress. However, most methods require
multi-view or multi-illumination information, which therefore put forward high
requirements for data acquisition. In this paper, we study the reconstruction
of high-fidelity 3D head models from arbitrary monocular videos. Non-rigid
structure from motion (NRSFM) methods have been widely used to solve such
problems according to the two-dimensional correspondence between different
frames. However, the inaccurate correspondence caused by high-complex hair
structures and various facial expression changes would heavily influence the
reconstruction accuracy. To tackle these problems, we propose a prior-guided
dynamic implicit neural network. Specifically, we design a two-part dynamic
deformation field to transform the current frame space to the canonical one. We
further model the head geometry in the canonical space with a learnable signed
distance field (SDF) and optimize it using the volumetric rendering with the
guidance of two-main head priors to improve the reconstruction accuracy and
robustness. Extensive ablation studies and comparisons with state-of-the-art
methods demonstrate the effectiveness and robustness of our proposed method.

从任意单眼视频中重建高保真 3D 头部模型的方法。设计了一个带有两个动态变形场的动态隐式神经网络，通过学习的有符号距离场在规范空间中建模头部几何形状，以提高重建精度和鲁棒性。与先进方法的大量消融研究和比较显示出我们提出的方法的有效性和鲁棒性。