  This paper proposes a new generative adversarial network for pose transfer,
i.e., transferring the pose of a given person to a target pose. We design a
progressive generator which comprises a sequence of transfer blocks. Each block
performs an intermediate transfer step by modeling the relationship between the
condition and the target poses with attention mechanism. Two types of blocks
are introduced, namely Pose-Attentional Transfer Block (PATB) and Aligned
Pose-Attentional Transfer Bloc ~(APATB). Compared with previous works, our
model generates more photorealistic person images that retain better appearance
consistency and shape consistency compared with input images. We verify the
efficacy of the model on the Market-1501 and DeepFashion datasets, using
quantitative and qualitative measures. Furthermore, we show that our method can
be used for data augmentation for the person re-identification task,
alleviating the issue of data insufficiency. Code and pretrained models are
available at https://github.com/tengteng95/Pose-Transfer.git.
