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Face parsing assigns pixel-wise semantic labels as the face representationfor computers, which is the fundamental part of many advanced facetechnologies. Compared with 2D face parsing, 3D face parsing shows morepotential to achieve better performance and further application, but it isstill challenging due to 3D mesh data computation. Recent works introduceddifferent methods for 3D surface segmentation, while the performance is stilllimited. In this paper, we propose a method based on the "3D-2D-3D" strategy toaccomplish 3D face parsing. The topological disk-like 2D face image containingspatial and textural information is transformed from the sampled 3D face datathrough the face parameterization algorithm, and a specific 2D network calledCPFNet is proposed to achieve the semantic segmentation of the 2D parameterizedface data with multi-scale technologies and feature aggregation. The 2Dsemantic result is then inversely re-mapped to 3D face data, which finallyachieves the 3D face parsing. Experimental results show that both CPFNet andthe "3D-2D-3D" strategy accomplish high-quality 3D face parsing and outperformstate-of-the-art 2D networks as well as 3D methods in both qualitative andquantitative comparisons.

3D Face Parsing via Surface Parameterization and 2D Semantic Segmentation Network