We propose a semi-supervised network for wide-angle portraits correction. Wide-angle images often suffer from skew and distortion affected by perspective distortion, especially noticeable at the face regions. Previous deep learning based approaches require the ground-truth correction flow maps for the training guidance. However, such labels are expensive, which can only be obtained manually. In this work, we propose a semi-supervised scheme, which can consume unlabeled data in addition to the labeled data for improvements. Specifically, our semi-supervised scheme takes the advantages of the consistency mechanism, with several novel components such as direction and range consistency (DRC) and regression consistency (RC). Furthermore, our network, named as Multi-Scale Swin-Unet (MS-Unet), is built upon the multi-scale swin transformer block (MSTB), which can learn both local-scale and long-range semantic information effectively. In addition, we introduce a high-quality unlabeled dataset with rich scenarios for the training. Extensive experiments demonstrate that the proposed method is superior over the state-of-the-art methods and other representative baselines.

提出一种半监督网络用于广角肖像矫正，利用一致性机制和多尺度Swin Transformer块构建MS-Unet网络，同时使用标记和未标记数据进行训练，实现了比现有方法更优异的结果。

多尺度变换器半监督广角人像校正