  Face parsing assigns pixel-wise semantic labels as the face representation
for computers, which is the fundamental part of many advanced face
technologies. Compared with 2D face parsing, 3D face parsing shows more
potential to achieve better performance and further application, but it is
still challenging due to 3D mesh data computation. Recent works introduced
different methods for 3D surface segmentation, while the performance is still
limited. In this paper, we propose a method based on the "3D-2D-3D" strategy to
accomplish 3D face parsing. The topological disk-like 2D face image containing
spatial and textural information is transformed from the sampled 3D face data
through the face parameterization algorithm, and a specific 2D network called
CPFNet is proposed to achieve the semantic segmentation of the 2D parameterized
face data with multi-scale technologies and feature aggregation. The 2D
semantic result is then inversely re-mapped to 3D face data, which finally
achieves the 3D face parsing. Experimental results show that both CPFNet and
the "3D-2D-3D" strategy accomplish high-quality 3D face parsing and outperform
state-of-the-art 2D networks as well as 3D methods in both qualitative and
quantitative comparisons.
