Pose guided synthesis aims to generate a new image in an arbitrary target
pose while preserving the appearance details from the source image. Existing
approaches rely on either hard-coded spatial transformations or 3D body
modeling. They often overlook complex non-rigid pose deformation or unmatched
occluded regions, thus fail to effectively preserve appearance information. In
this paper, we propose an unsupervised pose flow learning scheme that learns to
transfer the appearance details from the source image. Based on such learned
pose flow, we proposed GarmentNet and SynthesisNet, both of which use
multi-scale feature-domain alignment for coarse-to-fine synthesis. Experiments
on the DeepFashion, MVC dataset and additional real-world datasets demonstrate
that our approach compares favorably with the state-of-the-art methods and
generalizes to unseen poses and clothing styles.

本文提出了一种无监督姿势流学习方案，通过学习源图像的姿势流，进而使用多尺度特征域对齐进行粗到细的综合，在 DeepFashion,MVC 数据集和其他真实世界数据集上进行实验，结果表明该方法在保留外观信息方面效果显著，并且具有良好的泛化性，适用于不同的服装风格和姿势类型。