Progressive Pose Attention Transfer for Person Image Generation
Zhen Zhu, Tengteng Huang, Baoguang Shi, Miao Yu, Bofei Wang, Xiang Bai
Abstract
This paper proposes a new generative adversarial network to the problem of pose transfer, i.e., transferring the pose of a given person to a target one. 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.
BibTeX
@inproceedings{cvpr2019_progressiveposea,
title = {Progressive Pose Attention Transfer for Person Image Generation},
author = {Zhen Zhu and Tengteng Huang and Baoguang Shi and Miao Yu and Bofei Wang and Xiang Bai},
booktitle = {CVPR 2019},
year = {2019}
}