Pose-based person image synthesis aims to generate a new image containing a person with a target pose conditioned on a source image containing a person with a specified pose. It is challenging as the target pose is arbitrary and often significantly differs from the specified source pose, which leads to large appearance discrepancy between the source and the target images. This paper presents the Pose Transform Generative Adversarial Network (PoT-GAN) for person image synthesis where the generator explicitly learns the transform between the two poses by manipulating the corresponding multi-scale feature maps. By incorporating the learned pose transform information into the multi-scale feature maps of the source image in a GAN architecture, our method reliably transfers the appearance of the person in the source image to the target pose with no need for any hard-coded spatial information depicting the change of pose. According to both qualitative and quantitative results, the proposed PoT-GAN demonstrates a state-of-the-art performance on three publicly available datasets for person image synthesis.

中文翻译：

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PoT-GAN：用于人物图像合成的姿势变换 GAN

基于姿势的人物图像合成旨在以包含具有指定姿势的人物的源图像为条件，生成包含具有目标姿势的人物的新图像。这是具有挑战性的，因为目标姿态是任意的，并且通常与指定的源姿态显着不同，这导致源图像和目标图像之间的外观差异很大。本文介绍了用于人物图像合成的姿势变换生成对抗网络 (PoT-GAN)，其中生成器通过操纵相应的多尺度特征图显式地学习两个姿势之间的变换。通过将学习到的姿态变换信息合并到 GAN 架构中源图像的多尺度特征图中，我们的方法可靠地将源图像中人物的外观转移到目标姿势，而无需任何描述姿势变化的硬编码空间信息。根据定性和定量结果，所提出的 PoT-GAN 在用于人物图像合成的三个公开可用数据集上展示了最先进的性能。