We propose a novel method for high-quality facial texture reconstruction from RGB images using a novel capturing routine based on a single smartphone which we equip with an inexpensive polarization foil. Specifically, we turn the flashlight into a polarized light source and add a polarization filter on top of the camera. Leveraging this setup, we capture the face of a subject with cross-polarized and parallel-polarized light. For each subject, we record two short sequences in a dark environment under flash illumination with different light polarization using the modified smartphone. Based on these observations, we reconstruct an explicit surface mesh of the face using structure from motion. We then exploit the camera and light co-location within a differentiable renderer to optimize the facial textures using an analysis-by-synthesis approach. Our method optimizes for high-resolution normal textures, diffuse albedo, and specular albedo using a coarse-to-fine optimization scheme. We show that the optimized textures can be used in a standard rendering pipeline to synthesize high-quality photo-realistic 3D digital humans in novel environments.

翻译：我们提出了一种新颖的方法，用于从RGB图像中高质量重建面部纹理。该方法基于一种创新的采集流程，仅需配备低成本偏振膜片的智能手机即可实现。具体而言，我们将闪光灯改造为偏振光源，并在摄像头上添加偏振滤镜。利用该装置，我们通过交叉偏振与平行偏振光采集受试者的面部信息。针对每位受试者，我们使用改装后的智能手机在黑暗环境中，以不同偏振方向的闪光照明录制两段短序列。基于观测数据，我们通过运动恢复结构重建出显式的人脸表面网格。随后，利用可微渲染器中的相机与光源共定位特性，采用分析-合成策略优化面部纹理。方法采用由粗到细的优化方案，求解高分辨率法向纹理、漫反射反照率及镜面反照率。实验证明，优化后的纹理可直接用于标准渲染管线，在新环境中合成高质量、照片级逼真的三维数字人。