  Pose-guided person image generation and animation aim to transform a source
person image to target poses. These tasks require spatial manipulation 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. This framework first estimates global flow fields between
sources and targets. Then, corresponding local source feature patches are
sampled with content-aware local attention coefficients. We show that our
framework can spatially transform the inputs in an efficient manner. Meanwhile,
we further model the temporal consistency for the person image animation task
to generate coherent videos. The experiment results of both image generation
and animation tasks demonstrate the superiority of our model. Besides,
additional results of novel view synthesis and face image animation show that
our model is applicable to other tasks requiring spatial transformation. The
source code of our project is available at
https://github.com/RenYurui/Global-Flow-Local-Attention.
