  Pose guided person image generation means to generate a photo-realistic
person image conditioned on an input person image and a desired pose. This task
requires spatial manipulation of the source image according to the target pose.
However, the generative adversarial networks (GANs) widely used for image
generation and translation rely on spatially local and translation equivariant
operators, i.e., convolution, pooling and unpooling, which cannot handle large
image deformation. This paper introduces a novel two-stream appearance transfer
network (2s-ATN) to address this challenge. It is a multi-stage architecture
consisting of a source stream and a target stream. Each stage features an
appearance transfer module and several two-stream feature fusion modules. The
former finds the dense correspondence between the two-stream feature maps and
then transfers the appearance information from the source stream to the target
stream. The latter exchange local information between the two streams and
supplement the non-local appearance transfer. Both quantitative and qualitative
results indicate the proposed 2s-ATN can effectively handle large spatial
deformation and occlusion while retaining the appearance details. It
outperforms prior states of the art on two widely used benchmarks.
