# Mask-FPAN: Semi-Supervised Face Parsing in the Wild With De-Occlusion and UV GAN

Source: [Mask-FPAN: Semi-Supervised Face Parsing in the Wild With De-Occlusion and UV GAN](<https://www.sota2.com/research/paper/68fa6f8f1e209131cea46417>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:22:26.639Z

Authors: Lei Li, Tianfang Zhang, Zhongfeng Kang, Xikun Jiang

Published: 2022-12-18T14:51:46.000Z

## Abstract

Fine-grained semantic segmentation of a person's face and head, including facial parts and head components, has progressed a great deal in recent years. However, it remains a challenging task, whereby considering ambiguous occlusions and large pose variations are particularly difficult. To overcome these difficulties, we propose a novel framework termed Mask-FPAN. It uses a de-occlusion module that learns to parse occluded faces in a semi-supervised way. In particular, face landmark localization, face occlusionstimations, and detected head poses are taken into account. A 3D morphable face model combined with the UV GAN improves the robustness of 2D face parsing. In addition, we introduce two new datasets named FaceOccMask-HQ and CelebAMaskOcc-HQ for face paring work. The proposed Mask-FPAN framework addresses the face parsing problem in the wild and shows significant performance improvements with MIOU from 0.7353 to 0.9013 compared to the state-of-the-art on challenging face datasets.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2212.09098>)

[PDF](<https://arxiv.org/pdf/2212.09098.pdf>)