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:
this https URL

提出了一种新颖的双重任务姿势变换网络（DPTN），它将源自重构任务和变换任务结合起来，通过姿态变换模块（PTM）和共享部分权重来实现源到目标的生成，从而有效地提升了纹理映射，优于现有方法。

探索姿态引导的人物图像生成的双重任务相关性

Exploring Dual-task Correlation for Pose Guided Person Image Generation

In this paper we tackle the problem of pose guided person image generation,
which aims to transfer a person image from the source pose to a novel target
pose while maintaining the source appearance. Given the inefficiency of
standard CNNs in handling large spatial transformation, we propose a
structure-aware flow based method for high-quality person image generation.
Specifically, instead of learning the complex overall pose changes of human
body, we decompose the human body into different semantic parts (e.g., head,
torso, and legs) and apply different networks to predict the flow fields for
these parts separately. Moreover, we carefully design the network modules to
effectively capture the local and global semantic correlations of features
within and among the human parts respectively. Extensive experimental results
show that our method can generate high-quality results under large pose
discrepancy and outperforms state-of-the-art methods in both qualitative and
quantitative comparisons.

本文介绍了一种基于结构感知流的方法，利用语义部分将人体分解，来应对人物姿态变换并生成高质量图像，网络模块能够有效地捕捉人体局部和全局语义特征，实验结果表明，该方法在生成高质量图像方面胜过其他方法。