  Limited by the nature of the low-dimensional representational capacity of
3DMM, most of the 3DMM-based face reconstruction (FR) methods fail to recover
high-frequency facial details, such as wrinkles, dimples, etc. Some attempt to
solve the problem by introducing detail maps or non-linear operations, however,
the results are still not vivid. To this end, we in this paper present a novel
hierarchical representation network (HRN) to achieve accurate and detailed face
reconstruction from a single image. Specifically, we implement the geometry
disentanglement and introduce the hierarchical representation to fulfill
detailed face modeling. Meanwhile, 3D priors of facial details are incorporated
to enhance the accuracy and authenticity of the reconstruction results. We also
propose a de-retouching module to achieve better decoupling of the geometry and
appearance. It is noteworthy that our framework can be extended to a multi-view
fashion by considering detail consistency of different views. Extensive
experiments on two single-view and two multi-view FR benchmarks demonstrate
that our method outperforms the existing methods in both reconstruction
accuracy and visual effects. Finally, we introduce a high-quality 3D face
dataset FaceHD-100 to boost the research of high-fidelity face reconstruction.
The project homepage is at https://younglbw.github.io/HRN-homepage/.
