ML Anthology

Progressive Pose Attention Transfer for Person Image Generation

Zhen Zhu, Tengteng Huang, Baoguang Shi, Miao Yu, Bofei Wang, Xiang Bai
CVPR 2019
doi:10.1109/CVPR.2019.00245 /cvpr/2019/zhu2019cvpr-progressive/

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.

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Text

Zhu et al. "Progressive Pose Attention Transfer for Person Image Generation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00245

Markdown

[Zhu et al. "Progressive Pose Attention Transfer for Person Image Generation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/zhu2019cvpr-progressive/) doi:10.1109/CVPR.2019.00245

BibTeX

@inproceedings{zhu2019cvpr-progressive,
  title     = {{Progressive Pose Attention Transfer for Person Image Generation}},
  author    = {Zhu, Zhen and Huang, Tengteng and Shi, Baoguang and Yu, Miao and Wang, Bofei and Bai, Xiang},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2019},
  doi       = {10.1109/CVPR.2019.00245},
  url       = {https://mlanthology.org/cvpr/2019/zhu2019cvpr-progressive/}
}