Over the last years, many face analysis tasks have accomplished astounding performance, with applications including face generation and 3D face reconstruction from a single "in-the-wild" image. Nevertheless, to the best of our knowledge, there is no method which can produce render-ready high-resolution 3D faces from "in-the-wild" images and this can be attributed to the: (a) scarcity of available data for training, and (b) lack of robust methodologies that can successfully be applied on very high-resolution data. In this work, we introduce the first method that is able to reconstruct photorealistic render-ready 3D facial geometry and BRDF from a single "in-the-wild" image. We capture a large dataset of facial shape and reflectance, which we have made public. We define a fast facial photorealistic differentiable rendering methodology with accurate facial skin diffuse and specular reflection, self-occlusion and subsurface scattering approximation. With this, we train a network that disentangles the facial diffuse and specular BRDF components from a shape and texture with baked illumination, reconstructed with a state-of-the-art 3DMM fitting method. Our method outperforms the existing arts by a significant margin and reconstructs high-resolution 3D faces from a single low-resolution image, that can be rendered in various applications, and bridge the uncanny valley.

翻译：过去几年来,许多面部分析任务都取得了令人惊叹的性能,包括面部生成和3D面部面部改造等应用都从单一的“在动”图像中重建出。然而,据我们所知,我们没有方法能够从“在动”图像中产生现成的高分辨率 3D脸孔,这可以归因于:(a) 缺乏可用于培训的数据,以及(b) 缺乏能够成功地应用于甚高分辨率数据的可靠方法。在这项工作中,我们引入了第一种方法,能够从单一的“在动”图像中重建光现实化的3D面部成熟的3D面部和BRDF。我们采集了一张大张面部形状和反射的数据集,我们已经将其公诸于众。我们定义了一种快速的面部真实真实化的翻版方法,包括准确的面部皮肤扩散和视觉反射、自我封闭和地表层缩缩影的近似方法。我们训练了一个网络,将面部面部扩散和直观的BDFDF组件从非形状和纹状的3D图像和纹状图状图状图状重建了。我们用高分辨率的3MMMMD法重建了一种高分辨率的方法,通过高分辨率和高分辨率方法重建了现有。