  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.
