  In this paper, we present an end-to-end learning framework for detailed 3D
face reconstruction from a single image. Our approach uses a 3DMM-based coarse
model and a displacement map in UV-space to represent a 3D face. Unlike
previous work addressing the problem, our learning framework does not require
supervision of surrogate ground-truth 3D models computed with traditional
approaches. Instead, we utilize the input image itself as supervision during
learning. In the first stage, we combine a photometric loss and a facial
perceptual loss between the input face and the rendered face, to regress a
3DMM-based coarse model. In the second stage, both the input image and the
regressed texture of the coarse model are unwrapped into UV-space, and then
sent through an image-toimage translation network to predict a displacement map
in UVspace. The displacement map and the coarse model are used to render a
final detailed face, which again can be compared with the original input image
to serve as a photometric loss for the second stage. The advantage of learning
displacement map in UV-space is that face alignment can be explicitly done
during the unwrapping, thus facial details are easier to learn from large
amount of data. Extensive experiments demonstrate the superiority of the
proposed method over previous work.
