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.
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本文介绍了一种新的分层表示网络（HRN），通过几何分离和分层表示来实现对细节精确的面部重建。同时，考虑不同视角的细节一致性，提出了一种去除重新贴图模块。通过在两个单视图和两个多视图 FR 基准上的实验，展示了该方法在重建精度和视觉效果方面的优越性，并引入了高质量的 3D 面部数据集 FaceHD-100。