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18 November 2019 Attention-guided GANs for human pose transfer
Jinsong Zhang, Yuyang Zhao, Kun Li, Yebin Liu, Jingyu Yang, Qionghai Dai
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This paper presents a novel generative adversarial network for the task of human pose transfer, which aims at transferring the pose of a given person to a target pose. In order to deal with pixel-to-pixel misalignment due to the pose differences, we introduce an attention mechanism and propose Pose-Guided Attention Blocks. With these blocks, the generator can learn how to transfer the details from the conditional image to the target image based on the target pose. Our network can make the target pose truly guide the transfer of features. The effectiveness of the proposed network is validated on DeepFasion and Market-1501 datasets. Compared with state-of-the-art methods, our generated images are more realistic with better facial details.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jinsong Zhang, Yuyang Zhao, Kun Li, Yebin Liu, Jingyu Yang, and Qionghai Dai "Attention-guided GANs for human pose transfer", Proc. SPIE 11187, Optoelectronic Imaging and Multimedia Technology VI, 111870W (18 November 2019);

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