论文标题
面具 - fpan：半监督的面孔在野外通过去邻算和紫外线进行解析
Mask-FPAN: Semi-Supervised Face Parsing in the Wild With De-Occlusion and UV GAN
论文作者
论文摘要
近年来，对一个人的脸和头部的细粒语义细分，包括面部零件和头部组件，都取得了很大的进展。但是，这仍然是一项具有挑战性的任务，即考虑模棱两可的阻塞和巨大的姿势变化特别困难。为了克服这些困难，我们提出了一个称为“面具”的新框架。它使用了一个去嵌入的模块，该模块学会以半监督的方式解析面孔。特别是，要考虑到面部标志性的定位，面部遮挡刺激和检测到的头姿势。 3D可变形的面部模型与紫外线GAN相结合，可提高2D面对解析的稳健性。此外，我们介绍了两个名为faceoccmask-HQ和Celebamaskocc-HQ的新数据集，用于面部削皮工作。拟议的面具-FPAN框架解决了野外的面部解析问题，并且与具有挑战性的面部数据集相比，MIOU的性能从0.7353提高到0.9013。
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.
