This paper proposes a new generative adversarial network for pose transfer, i.e., transferring the pose of a given person to a target pose. We design a progressive generator which comprises a sequence of transfer blocks. Each block performs an intermediate transfer step by modeling the relationship between the condition and the target poses with attention mechanism. Two types of blocks are introduced, namely Pose-Attentional Transfer Block (PATB) and Aligned Pose-Attentional Transfer Bloc ~(APATB). Compared with previous works, our model generates more photorealistic person images that retain better appearance consistency and shape consistency compared with input images. We verify the efficacy of the model on the Market-1501 and DeepFashion datasets, using quantitative and qualitative measures. Furthermore, we show that our method can be used for data augmentation for the person re-identification task, alleviating the issue of data insufficiency. Code and pretrained models are available at https://github.com/tengteng95/Pose-Transfer.git.
@article{arxiv.2103.11622,
title = {Progressive and Aligned Pose Attention Transfer for Person Image Generation},
author = {Zhen Zhu and Tengteng Huang and Mengde Xu and Baoguang Shi and Wenqing Cheng and Xiang Bai},
journal= {arXiv preprint arXiv:2103.11622},
year = {2021}
}
Comments
Accepted by TPAMI. An extension of the conference version. Conference version at arXiv:1904.03349