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:由于3DMM(3DMM)的低维代表能力性质的限制,3DMM(3DMM)基于面部重建(FR)方法大多无法恢复高频面部细节,例如皱纹、凹洞等。有些试图通过引入详细地图或非线性操作来解决问题,但结果仍然不生动。为此,我们本文提出了一个新的等级代表网络,从单一图像中实现准确和详细的面部重建。具体地说,我们实施几何分解,并引入等级代表,以完成详细的面部模型。与此同时,还纳入了3D面部细节的前身,以提高重建结果的准确性和真实性。我们还提议了一个脱钩模块,以更好地分解几何和外观。值得注意的是,通过考虑不同观点的详细一致性,我们的框架可以扩大到多视角。关于两个单一视角和两个多视角FR基准的广泛实验表明,我们的方法在重建准确性和视觉效果两方面都超越了现有方法。最后,我们提出了一个高质量的3D面面面面面数据系统。我们引入了高质量的3D的面面面的重建研究。</s>