Generating photorealistic images of human subjects in any unseen pose have
crucial applications in generating a complete appearance model of the subject.
However, from a computer vision perspective, this task becomes significantly
challenging due to the inability of modelling the data distribution conditioned
on pose. Existing works use a complicated pose transformation model with
various additional features such as foreground segmentation, human body parsing
etc. to achieve robustness that leads to computational overhead. In this work,
we propose a simple yet effective pose transformation GAN by utilizing the
Residual Learning method without any additional feature learning to generate a
given human image in any arbitrary pose. Using effective data augmentation
techniques and cleverly tuning the model, we achieve robustness in terms of
illumination, occlusion, distortion and scale. We present a detailed study,
both qualitative and quantitative, to demonstrate the superiority of our model
over the existing methods on two large datasets.

利用残差学习的方法，结合有效的数据增强技术，提出一种简单而有效的姿态转换 GAN，在不需使用额外功能学习的情况下，在任意姿态下生成给定的人体图像。该方法在两个大型数据集上展现出比现有方法更优越的性能，实现了在光照、遮挡、失真和尺度方面的鲁棒性。