  This paper proposes a new generative adversarial network for pose transfer,
i.e., transferring the pose of a given person to a target pose. The generator
of the network comprises a sequence of Pose-Attentional Transfer Blocks that
each transfers certain regions it attends to, generating the person image
progressively. Compared with those in previous works, our generated person
images possess better appearance consistency and shape consistency with the
input images, thus significantly more realistic-looking. The efficacy and
efficiency of the proposed network are validated both qualitatively and
quantitatively on Market-1501 and DeepFashion. Furthermore, the proposed
architecture can generate training images for person re-identification,
alleviating data insufficiency. Codes and models are available at:
https://github.com/tengteng95/Pose-Transfer.git.
