While the problem of estimating shapes and diffuse reflectances of human faces from images has been extensively studied, there is relatively less work done on recovering the specular albedo. This paper presents a lightweight solution for inferring photorealistic facial reflectance and geometry. Our system processes video streams from two views of a subject, and outputs two reflectance maps for diffuse and specular albedos, as well as a vector map of surface normals. A model-based optimization approach is used, consisting of the three stages of multi-view face model fitting, facial reflectance inference and facial geometry refinement. Our approach is based on a novel formulation built upon the 3D morphable model (3DMM) for representing 3D textured faces in conjunction with the Blinn-Phong reflection model. It has the advantage of requiring only a simple setup with two video streams, and is able to exploit the interaction between the diffuse and specular reflections across multiple views as well as time frames. As a result, the method is able to reliably recover high-fidelity facial reflectance and geometry, which facilitates various applications such as generating photorealistic facial images under new viewpoints or illumination conditions.

虽然已经广泛研究了从图像估计人脸的形状和漫反射率的问题，但是在恢复镜面反射率方面所做的工作相对较少。本文提出了一个轻量级的推断逼真的面部反射率和几何形状的解决方案。我们的系统从一个对象的两个视角处理视频流，并输出两个漫反射和镜面反射率的反射率贴图，以及一个表面法线的矢量贴图。使用基于模型的优化方法，该方法包括多视图面部模型拟合，面部反射推断和面部几何形状优化三个阶段。我们的方法基于一种基于3D可变形模型（3DMM）的新颖公式，该模型可与Blinn-Phong反射模型结合使用来表示3D纹理面。它的优点是只需要简单地设置两个视频流，并能够利用多个视图以及时间范围的漫反射和镜面反射之间的相互作用。结果是，