  This paper proposes the novel Pose Guided Person Generation Network (PG$^2$)
that allows to synthesize person images in arbitrary poses, based on an image
of that person and a novel pose. Our generation framework PG$^2$ utilizes the
pose information explicitly and consists of two key stages: pose integration
and image refinement. In the first stage the condition image and the target
pose are fed into a U-Net-like network to generate an initial but coarse image
of the person with the target pose. The second stage then refines the initial
and blurry result by training a U-Net-like generator in an adversarial way.
Extensive experimental results on both 128$\times$64 re-identification images
and 256$\times$256 fashion photos show that our model generates high-quality
person images with convincing details.
