Conference item
Bipartite graph reasoning GANs for person pose and facial image synthesis
- Abstract:
- We present a novel bipartite graph reasoning Generative Adversarial Network (BiGraphGAN) for two challenging tasks: person pose and facial image synthesis. The proposed graph generator 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 long-range cross relations between the source and target pose in a bipartite graph, which mitigates some of the challenges caused by pose deformation. Moreover, we propose a new interaction-andaggregation (IA) block to effectively update and enhance the feature representation capability of both a person’s shape and appearance in an interactive way. To further capture the change in pose of each part more precisely, we propose a novel part-aware bipartite graph reasoning (PBGR) block to decompose the task of reasoning the global structure transformation with a bipartite graph into learning different local transformations for different semantic body/face parts. Experiments on two challenging generation tasks with three public datasets demonstrate the effectiveness of the proposed methods in terms of objective quantitative scores and subjective visual realness. The source code and trained models are available at https://github.com/ Ha0Tang/BiGraphGAN.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
-
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(Preview, Accepted manuscript, pdf, 7.5MB, Terms of use)
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- Publisher copy:
- 10.1007/s11263-022-01722-5
Authors
- Publisher:
- Springer
- Journal:
- International Journal of Computer Vision More from this journal
- Volume:
- 131
- Pages:
- 644–658
- Publication date:
- 2022-12-08
- Acceptance date:
- 2022-06-19
- Event title:
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Event location:
- New Orleans, LA, USA
- Event website:
- https://cvpr2022.thecvf.com/
- Event start date:
- 2022-06-19
- Event end date:
- 2022-06-24
- DOI:
- EISSN:
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1573-1405
- ISSN:
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0920-5691
- Language:
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English
- Keywords:
- Pubs id:
-
1311032
- Local pid:
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pubs:1311032
- Deposit date:
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2022-12-02
Terms of use
- Copyright holder:
- Tang et al
- Copyright date:
- 2022
- Rights statement:
- © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
- Notes:
- This paper was presented at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 19th-24th June 2022, New Orleans, LA, USA. This is the accepted manuscript version of the article. The final version is available online from Springer at: https://dx.doi.org/10.1007/s11263-022-01722-5
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