  In this paper we address the problem of generating person images conditioned
on a given pose. Specifically, given an image of a person and a target pose, we
synthesize a new image of that person in the novel pose. In order to deal with
pixel-to-pixel misalignments caused by the pose differences, we introduce
deformable skip connections in the generator of our Generative Adversarial
Network. Moreover, a nearest-neighbour loss is proposed instead of the common
L1 and L2 losses in order to match the details of the generated image with the
target image. We test our approach using photos of persons in different poses
and we compare our method with previous work in this area showing
state-of-the-art results in two benchmarks. Our method can be applied to the
wider field of deformable object generation, provided that the pose of the
articulated object can be extracted using a keypoint detector.
