Three-dimensional face dense alignment and reconstruction in the wild is a
challenging problem as partial facial information is commonly missing in
occluded and large pose face images. Large head pose variations also increase
the solution space and make the modeling more difficult. Our key idea is to
model occlusion and pose to decompose this challenging task into several
relatively more manageable subtasks. To this end, we propose an end-to-end
framework, termed as Self-aligned Dual face Regression Network (SADRNet), which
predicts a pose-dependent face, a pose-independent face. They are combined by
an occlusion-aware self-alignment to generate the final 3D face. Extensive
experiments on two popular benchmarks, AFLW2000-3D and Florence, demonstrate
that the proposed method achieves significant superior performance over
existing state-of-the-art methods.

本文提出了一种自对齐的双重脸部回归网络 (SADRNet) 框架，通过建模遮挡和姿势，将三维面部的密集对齐和重建问题分解为几个相对更易于处理的子任务，分别预测一个姿态相关面和一个姿态无关面，再由遮挡感知的自对齐组合生成最终的三维面部，实验结果表明该方法在 AFLW2000-3D 和 Florence 等基准测试上显著优于现有最先进方法。