Papers › Bipartite Graph Reasoning GANs for Person Image Generation

Bipartite Graph Reasoning GANs for Person Image Generation

10 Aug 2020arXiv:2008.04381archive 2025-07-28

Hao Tang, Song Bai, Philip H. S. Torr, Nicu Sebe

We present a novel Bipartite Graph Reasoning GAN (BiGraphGAN) for the challenging person image generation task. The proposed graph generator mainly consists of two novel blocks that aim to model the pose-to-pose and pose-to-image relations, respectively. Specifically, the proposed Bipartite Graph Reasoning (BGR) block aims to reason the crossing long-range relations between the source pose and the target pose in a bipartite graph, which mitigates some challenges caused by pose deformation. Moreover, we propose a new Interaction-and-Aggregation (IA) block to effectively update and enhance the feature representation capability of both person's shape and appearance in an interactive way. Experiments on two challenging and public datasets, i.e., Market-1501 and DeepFashion, show the effectiveness of the proposed BiGraphGAN in terms of objective quantitative scores and subjective visual realness. The source code and trained models are available at https://github.com/Ha0Tang/BiGraphGAN.

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Tasks

Image GenerationPose Transfer

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Pose Transfer Deep-Fashion BiGraphGAN IS 3.430 #3 of 12 Archive leaderboard report
Pose Transfer Deep-Fashion BiGraphGAN PCKh 0.97 #3 of 12 Archive leaderboard report
Pose Transfer Deep-Fashion BiGraphGAN SSIM 0.778 #3 of 12 Archive leaderboard report
Pose Transfer Market-1501 BiGraphGAN IS 3.329 #3 of 3 Archive leaderboard report
Pose Transfer Market-1501 BiGraphGAN PCKh 0.94 #3 of 3 Archive leaderboard report
Pose Transfer Market-1501 BiGraphGAN SSIM 0.325 #3 of 3 Archive leaderboard report
Pose Transfer Market-1501 BiGraphGAN mask-IS 3.695 #3 of 3 Archive leaderboard report
Pose Transfer Market-1501 BiGraphGAN mask-SSIM 0.818 #3 of 3 Archive leaderboard report

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