Recent works in implicit representations, such as Neural Radiance Fields
(NeRF), have advanced the generation of realistic and animatable head avatars
from video sequences. These implicit methods are still confronted by visual
artifacts and jitters, since the lack of explicit geometric constraints poses a
fundamental challenge in accurately modeling complex facial deformations. In
this paper, we introduce Dynamic Tetrahedra (DynTet), a novel hybrid
representation that encodes explicit dynamic meshes by neural networks to
ensure geometric consistency across various motions and viewpoints. DynTet is
parameterized by the coordinate-based networks which learn signed distance,
deformation, and material texture, anchoring the training data into a
predefined tetrahedra grid. Leveraging Marching Tetrahedra, DynTet efficiently
decodes textured meshes with a consistent topology, enabling fast rendering
through a differentiable rasterizer and supervision via a pixel loss. To
enhance training efficiency, we incorporate classical 3D Morphable Models to
facilitate geometry learning and define a canonical space for simplifying
texture learning. These advantages are readily achievable owing to the
effective geometric representation employed in DynTet. Compared with prior
works, DynTet demonstrates significant improvements in fidelity, lip
synchronization, and real-time performance according to various metrics. Beyond
producing stable and visually appealing synthesis videos, our method also
outputs the dynamic meshes which is promising to enable many emerging
applications.

通过神经网络对显式动态网格进行编码的混合表示方法 Dynamic Tetrahedra (DynTet) 提供了一种有效的几何一致性，解决了在准确建模复杂面部变形方面缺乏显式几何约束的困境。与以往的工作相比，DynTet 在保真度、口型同步和实时性能等多个指标上表现出了显著的改进，同时还能输出动态网格并有很多新兴应用的潜力。

学习动态四面体进行高质量的说话头部合成

Learning Dynamic Tetrahedra for High-Quality Talking Head Synthesis

Most 3D face reconstruction methods rely on 3D morphable models, which
disentangle the space of facial deformations into identity geometry,
expressions and skin reflectance. These models are typically learned from a
limited number of 3D scans and thus do not generalize well across different
identities and expressions. We present the first approach to learn complete 3D
models of face identity geometry, albedo and expression just from images and
videos. The virtually endless collection of such data, in combination with our
self-supervised learning-based approach allows for learning face models that
generalize beyond the span of existing approaches. Our network design and loss
functions ensure a disentangled parameterization of not only identity and
albedo, but also, for the first time, an expression basis. Our method also
allows for in-the-wild monocular reconstruction at test time. We show that our
learned models better generalize and lead to higher quality image-based
reconstructions than existing approaches.

本研究提出了一种基于自监督学习的方法，通过图像和视频数据学习脸部特征的完整 3D 模型，实现了环境无关的单目重建，并训练出能够更好地推广和提高图像重建质量的表情基础等方面的面部模型。