Abstract
This paper proposes a new generative adversarial network for pose transfer, i.e., transferring the pose of a given person to a target pose. The generator of the network comprises a sequence of Pose-Attentional Transfer Blocks that each transfers certain regions it attends to, generating the person image progressively. Compared with those in previous works, our generated person images possess better appearance consistency and shape consistency with the input images, thus significantly more realistic-looking. The efficacy and efficiency of the proposed network are validated both qualitatively and quantitatively on Market-1501 and DeepFashion. Furthermore, the proposed architecture can generate training images for person re-identification, alleviating data insufficiency. Codes and models are available at: this https URL.
Abstract (translated)
本文提出了一种新的生成对抗式姿态转移网络，即将给定人的姿态转移到目标姿态。该网络的生成器包括一系列姿态-注意力传递块，每个传递块传递它所关注的特定区域，逐步生成人的图像。与以前的作品相比，我们生成的人物图像与输入图像具有更好的外观一致性和形状一致性，因此更具现实感。在市场1501和深度清洗中，定性和定量地验证了该网络的有效性和效率。此外，该体系结构还可以生成训练图像进行人员再识别，减轻了数据不足的问题。代码和模型可从以下网址获得：此https URL。
URL
https://arxiv.org/abs/1904.03349
