  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 the lack of 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.
