mlsanityData to 7 Oct 2026

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

arXiv 2206.09221 ↗First posted 18 Jun 2022cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 32 of its 67 references that are in the map. First posted on arXiv 18 Jun 2022.

Builds on

32 of 67 references.

References · 32

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20 more
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  1. 2024Gaussian Differentially Private Human Faces Under a Face Radial Curve Representationcited by 0