Parametric 3D models have enabled a wide variety of computer vision and
graphics tasks, such as modeling human faces, bodies and hands. In 3D face
modeling, 3DMM is the most widely used parametric model, but can't generate
fine geometric details solely from identity and expression inputs. To tackle
this limitation, we propose a neural parametric model named DNPM for the facial
geometric details, which utilizes deep neural network to extract latent codes
from facial displacement maps encoding details and wrinkles. Built upon DNPM, a
novel 3DMM named Detailed3DMM is proposed, which augments traditional 3DMMs by
including the synthesis of facial details only from the identity and expression
inputs. Moreover, we show that DNPM and Detailed3DMM can facilitate two
downstream applications: speech-driven detailed 3D facial animation and 3D face
reconstruction from a degraded image. Extensive experiments have shown the
usefulness of DNPM and Detailed3DMM, and the progressiveness of two proposed
applications.

提出了一种名为 DNPM 的神经参数化模型，用于面部几何细节，它利用深度神经网络从编码细节和皱纹的面部位移图中提取潜在代码。在 DNPM 的基础上，提出了一种名为 Detailed3DMM 的新型 3DMM，通过仅使用身份和表情输入进行面部细节的合成来改进传统的 3DMM。另外，我们展示了 DNPM 和 Detailed3DMM 在两个下游应用中的作用：基于语音驱动的详细 3D 面部动画和从降质图像中进行 3D 面部重建。广泛的实验证明了 DNPM 和 Detailed3DMM 的有用性以及两种提出的应用的进展性。