Realistic face rendering from multi-view images is beneficial to various computer vision and graphics applications. Due to the complex spatially-varying reflectance properties and geometry characteristics of faces, however, it remains challenging to recover 3D facial representations both faithfully and efficiently in the current studies. This paper presents a novel 3D face rendering model, namely NeuFace, to learn accurate and physically-meaningful underlying 3D representations by neural rendering techniques. It naturally incorporates the neural BRDFs into physically based rendering, capturing sophisticated facial geometry and appearance clues in a collaborative manner. Specifically, we introduce an approximated BRDF integration and a simple yet new low-rank prior, which effectively lower the ambiguities and boost the performance of the facial BRDFs. Extensive experiments demonstrate the superiority of NeuFace in human face rendering, along with a decent generalization ability to common objects.

翻译：多视角图像的真实感人脸渲染对计算机视觉与图形学的各类应用具有重要意义。然而，由于人脸具有复杂的空间变化反射属性与几何特性，现有研究在兼顾真实性与高效性的三维人脸表征恢复方面仍面临挑战。本文提出一种名为NeuFace的新型三维人脸渲染模型，通过神经渲染技术学习精确且具有物理意义的底层三维表征。该模型将神经BRDF自然融入基于物理的渲染框架，以协同方式捕捉复杂的人脸几何与外观线索。具体而言，我们引入了近似BRDF积分方法及简洁新颖的低秩先验，有效降低了人脸BRDF的歧义性并提升了性能。大量实验表明，NeuFace在人脸渲染任务中具有显著优势，同时展现出对通用物体的良好泛化能力。