This paper presents a Geometric-Photometric Joint Alignment(GPJA) method, for
accurately aligning human expressions by combining geometry and photometric
information. Common practices for registering human heads typically involve
aligning landmarks with facial template meshes using geometry processing
approaches, but often overlook photometric consistency. GPJA overcomes this
limitation by leveraging differentiable rendering to align vertices with target
expressions, achieving joint alignment in geometry and photometric appearances
automatically, without the need for semantic annotation or aligned meshes for
training. It features a holistic rendering alignment strategy and a multiscale
regularized optimization for robust and fast convergence. The method utilizes
derivatives at vertex positions for supervision and employs a gradient-based
algorithm which guarantees smoothness and avoids topological defects during the
geometry evolution. Experimental results demonstrate faithful alignment under
various expressions, surpassing the conventional ICP-based methods and the
state-of-the-art deep learning based method. In practical, our method enhances
the efficiency of obtaining topology-consistent face models from multi-view
stereo facial scanning.

本文提出了一种几何光度联合对齐（GPJA）方法，通过结合几何和光度信息，精确对齐人类表情。该方法利用可微渲染将顶点与目标表情对齐，实现几何和光度外观的联合对齐，无需语义注释或对齐网格进行训练，具有整体渲染对齐策略和多尺度正则化优化，获得健壮和快速的收敛性。实验结果表明，在各种表情下实现了准确对齐，超越了传统的基于 ICP 的方法和最先进的基于深度学习的方法。在实际应用中，我们的方法提高了从多视角立体人脸扫描中获取拓扑一致的人脸模型的效率。