Pose-guided person image generation is to transform a source person image to a target pose. This task requires spatial manipulations of source data. However, Convolutional Neural Networks are limited by lacking the ability to spatially transform the inputs. In this paper, we propose a differentiable global-flow local-attention framework to reassemble the inputs at the feature level. Specifically, our model first calculates the global correlations between sources and targets to predict flow fields. Then, the flowed local patch pairs are extracted from the feature maps to calculate the local attention coefficients. Finally, we warp the source features using a content-aware sampling method with the obtained local attention coefficients. The results of both subjective and objective experiments demonstrate the superiority of our model. Besides, additional results in video animation and view synthesis show that our model is applicable to other tasks requiring spatial transformation. Our source code is available at https://github.com/RenYurui/Global-Flow-Local-Attention.

本文提出一种不同于传统CNN的可微分全局流本地注意力框架来进行姿势引导的人体图像生成，该模型通过预测流场来进行全局相应，通过提取特征图中的局部补丁对局部注意力系数进行计算来生成新的人体图像，实验结果表明该方法优于传统方法，同时也适用于其他要求空间变换的任务。

深度图像空间变形用于人物图像生成