Wide-angle portraits often enjoy expanded views. However, they contain
perspective distortions, especially noticeable when capturing group portrait
photos, where the background is skewed and faces are stretched. This paper
introduces the first deep learning based approach to remove such artifacts from
freely-shot photos. Specifically, given a wide-angle portrait as input, we
build a cascaded network consisting of a LineNet, a ShapeNet, and a transition
module (TM), which corrects perspective distortions on the background, adapts
to the stereographic projection on facial regions, and achieves smooth
transitions between these two projections, accordingly. To train our network,
we build the first perspective portrait dataset with a large diversity in
identities, scenes and camera modules. For the quantitative evaluation, we
introduce two novel metrics, line consistency and face congruence. Compared to
the previous state-of-the-art approach, our method does not require camera
distortion parameters. We demonstrate that our approach significantly
outperforms the previous state-of-the-art approach both qualitatively and
quantitatively.

本研究介绍了第一个基于深度学习的方法，用于去除自由拍摄的广角人像照片中的透视失真和形变等因素，建立了一个包括 LineNet，ShapeNet 和 transition module（TM）的级联网络，并提出了两个新的度量标准：线条一致性和面部一致性，相比之前的最先进方法，本方法不需要相机失真参数，能够在身份、场景和相机模块的大多数多样性方面得到较好的性能。