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.

翻译：受限于三维形变模型（3DMM）低维表征能力的本质，大多数基于3DMM的人脸重建方法难以恢复高频面部细节，例如皱纹、酒窝等。尽管已有研究尝试通过引入细节图或非线性操作来解决该问题，但重建结果仍不够逼真。为此，本文提出了一种新颖的分层表征网络（HRN），以从单张图像实现精确且细节丰富的人脸重建。具体而言，我们通过几何解耦并引入分层表征来完善细节化人脸建模；同时，融合面部细节的三维先验信息以提升重建结果的准确性与真实性。此外，我们提出一种去修饰模块，以实现几何与外观的更好解耦。值得关注的是，本框架可通过考虑不同视角的细节一致性扩展至多视图模式。在单视图与多视图两类基准数据集上的大量实验表明，本方法在重建精度与视觉效果上均优于现有方法。最后，我们发布了一个高质量三维人脸数据集FaceHD-100，以推动高保真人脸重建研究。