3D Morphable Models (3DMMs) provide promising 3D face reconstructions in
various applications. However, existing methods struggle to reconstruct faces
with extreme expressions due to deficiencies in supervisory signals, such as
sparse or inaccurate landmarks. Segmentation information contains effective
geometric contexts for face reconstruction. Certain attempts intuitively depend
on differentiable renderers to compare the rendered silhouettes of
reconstruction with segmentation, which is prone to issues like local optima
and gradient instability. In this paper, we fully utilize the facial part
segmentation geometry by introducing Part Re-projection Distance Loss (PRDL).
Specifically, PRDL transforms facial part segmentation into 2D points and
re-projects the reconstruction onto the image plane. Subsequently, by
introducing grid anchors and computing different statistical distances from
these anchors to the point sets, PRDL establishes geometry descriptors to
optimize the distribution of the point sets for face reconstruction. PRDL
exhibits a clear gradient compared to the renderer-based methods and presents
state-of-the-art reconstruction performance in extensive quantitative and
qualitative experiments. The project will be publicly available.

本研究通过引入面部部分分割几何特征并利用 Part Re-projection Distance Loss (PRDL) 来优化点集的分布，从而在面部重建方面展现出最先进的性能。

基于面部局部分割的三维人脸重建

3D Face Reconstruction with the Geometric Guidance of Facial Part  Segmentation

Parsing human poses in images is fundamental in extracting critical visual
information for artificial intelligent agents. Our goal is to learn
self-contained body part representations from images, which we call visual
symbols, and their symbol-wise geometric contexts in this parsing process. Each
symbol is individually learned by categorizing visual features leveraged by
geometric information. In the categorization, we use Latent Support Vector
Machine followed by an efficient cross validation procedure to learn visual
symbols. Then, these symbols naturally define geometric contexts of body parts
in a fine granularity. When the structure of the compositional parts is a tree,
we derive an efficient approach to estimating human poses in images.
Experiments on two large datasets suggest our approach outperforms state of the
art methods.

该研究旨在从图像中学习自包含的身体部位表示形式（即称为视觉符号）及其符号化的几何上下文，以此来解析人类姿势，并利用潜在支持向量机和有效的交叉验证过程来对视觉符号进行分类，实现对肢体部位的精细分类。当姿势的构造部分是一棵树时，我们提出了一种有效的方法来估算图像中的人体姿势，并通过两个大型数据集的实验表明，该方法优于现有的方法。