  Pose Guided Person Image Generation (PGPIG) is the task of transforming a
person image from the source pose to a given target pose. Most of the existing
methods only focus on the ill-posed source-to-target task and fail to capture
reasonable texture mapping. To address this problem, we propose a novel
Dual-task Pose Transformer Network (DPTN), which introduces an auxiliary task
(i.e., source-to-source task) and exploits the dual-task correlation to promote
the performance of PGPIG. The DPTN is of a Siamese structure, containing a
source-to-source self-reconstruction branch, and a transformation branch for
source-to-target generation. By sharing partial weights between them, the
knowledge learned by the source-to-source task can effectively assist the
source-to-target learning. Furthermore, we bridge the two branches with a
proposed Pose Transformer Module (PTM) to adaptively explore the correlation
between features from dual tasks. Such correlation can establish the
fine-grained mapping of all the pixels between the sources and the targets, and
promote the source texture transmission to enhance the details of the generated
target images. Extensive experiments show that our DPTN outperforms
state-of-the-arts in terms of both PSNR and LPIPS. In addition, our DPTN only
contains 9.79 million parameters, which is significantly smaller than other
approaches. Our code is available at:
https://github.com/PangzeCheung/Dual-task-Pose-Transformer-Network.
