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- W2964436131 abstract "Manipulating person images under diverse poses, which transfers a person from one pose to another desired pose, is an interesting yet challenging task due to large non-rigid spatial deformation. Most existing works fail to preserve the fine-grained appearance consistency along with the pose changes due to the lack of explicit constraints and spatial modeling, leading to unrealistic results with severe artifacts. In this paper, we propose a novel Part-Preserving Generative Adversarial Network (PP-GAN) to achieve good manipulation quality by explicitly enforcing rich structure constraints over generative modeling. PP-GAN is proposed to decompose the challenging spatial transformation of the whole body into fine-grained part-level transformations, which are then integrated via human joint structure constraint. Given arbitrary poses, PP-GAN integrates human joint structure and region-level part cues as inputs to perform explicit generative modeling. Besides, we introduce a parsing-consistent loss to enforce semantic consistency among images with diverse poses, which guides the image synthesis from a semantic perspective. Extensive qualitative and quantitative evaluations on two benchmarks show that our PP-GAN significantly outperforms the state-of-the-art baselines in generating more realistic and plausible image synthesis results. PP-GAN successfully preserves part-level characteristics even for most challenging pose changes while prior works are easy to fail." @default.
- W2964436131 created "2019-08-13" @default.
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- W2964436131 date "2019-07-01" @default.
- W2964436131 modified "2023-09-27" @default.
- W2964436131 title "Part-Preserving Pose Manipulation for Person Image Synthesis" @default.
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- W2964436131 doi "https://doi.org/10.1109/icme.2019.00215" @default.
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