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Pose Guided Person Image Generation (PGPIG) is the task of transforming aperson image from the source pose to a given target pose. Most of the existingmethods only focus on the ill-posed source-to-target task and fail to capturereasonable texture mapping. To address this problem, we propose a novelDual-task Pose Transformer Network (DPTN), which introduces an auxiliary task(i.e., source-to-source task) and exploits the dual-task correlation to promotethe performance of PGPIG. The DPTN is of a Siamese structure, containing asource-to-source self-reconstruction branch, and a transformation branch forsource-to-target generation. By sharing partial weights between them, theknowledge learned by the source-to-source task can effectively assist thesource-to-target learning. Furthermore, we bridge the two branches with aproposed Pose Transformer Module (PTM) to adaptively explore the correlationbetween features from dual tasks. Such correlation can establish thefine-grained mapping of all the pixels between the sources and the targets, andpromote the source texture transmission to enhance the details of the generatedtarget images. Extensive experiments show that our DPTN outperformsstate-of-the-arts in terms of both PSNR and LPIPS. In addition, our DPTN onlycontains 9.79 million parameters, which is significantly smaller than otherapproaches. Our code is available at:https://github.com/PangzeCheung/Dual-task-Pose-Transformer-Network.

Exploring Dual-task Correlation for Pose Guided Person Image Generation