Abstract
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 realisticlooking. 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.