Papers › Progressive Pose Attention Transfer for Person Image Generation

Progressive Pose Attention Transfer for Person Image Generation

6 Apr 2019CVPR 2019 6arXiv:1904.03349archive 2025-07-28

Zhen Zhu, Tengteng Huang, Baoguang Shi, Miao Yu, Bofei Wang, Xiang Bai

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: https://github.com/tengteng95/Pose-Transfer.git.

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tengteng95/Pose-Transfer officialmentioned in papermentioned on GitHubpytorchMIT report
zsypotter/pose_transfer_keypoint mentioned on GitHubpytorch report

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addBounding tengteng95/Pose-Transfer/ssd_score/compute_ssd_score_fashion.py official repository unverified MIT (permissive) · 60b258f4dc8a7d3d · report
cords_to_map tengteng95/Pose-Transfer/tool/generate_pose_map_fashion.py official repository unverified MIT (permissive) · 53d6f76c39c24dd4 · report
cords_to_map tengteng95/Pose-Transfer/tool/generate_pose_map_market.py official repository unverified MIT (permissive) · b1180deb4573e01e · report
create_model tengteng95/Pose-Transfer/models/models.py official repository unverified MIT (permissive) · 53aa5b40d1658f67 · report
get_inception_score tengteng95/Pose-Transfer/tool/inception_score.py official repository unverified MIT (permissive) · ca6c3975c982ec08 · report
get_norm_layer tengteng95/Pose-Transfer/models/networks.py official repository unverified MIT (permissive) · ce809996d1428958 · report
get_scheduler tengteng95/Pose-Transfer/models/networks.py official repository unverified MIT (permissive) · f4ecb472fd266452 · report
load_pose_cords_from_strings tengteng95/Pose-Transfer/tool/generate_pose_map_fashion.py official repository unverified MIT (permissive) · 1fb3b75115e8317e · report
map_to_cord tengteng95/Pose-Transfer/tool/pose_utils.py official repository unverified MIT (permissive) · 0a24cbfa899d5c09 · report

Tasks

Image GenerationPerson Re-IdentificationPose Transfer

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Pose Transfer Deep-Fashion Progressive Pose Attention DS 0.976 #5 of 12 Archive leaderboard report
Pose Transfer Deep-Fashion Progressive Pose Attention IS 3.209 #5 of 12 Archive leaderboard report
Pose Transfer Deep-Fashion Progressive Pose Attention PCKh 0.96 #5 of 12 Archive leaderboard report
Pose Transfer Deep-Fashion Progressive Pose Attention Retrieval Top10 Recall 17.84 #5 of 12 Archive leaderboard report
Pose Transfer Deep-Fashion Progressive Pose Attention SSIM 0.773 #5 of 12 Archive leaderboard report
Pose Transfer Market-1501 Progressive Pose Attention DS 0.74 #1 of 3 Archive leaderboard report
Pose Transfer Market-1501 Progressive Pose Attention IS 3.323 #1 of 3 Archive leaderboard report
Pose Transfer Market-1501 Progressive Pose Attention PCKh 0.94 #1 of 3 Archive leaderboard report
Pose Transfer Market-1501 Progressive Pose Attention SSIM 0.311 #1 of 3 Archive leaderboard report
Pose Transfer Market-1501 Progressive Pose Attention mask-IS 3.773 #1 of 3 Archive leaderboard report
Pose Transfer Market-1501 Progressive Pose Attention mask-SSIM 0.811 #1 of 3 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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