  Pose transfer refers to the probabilistic image generation of a person with a
previously unseen novel pose from another image of that person having a
different pose. Due to potential academic and commercial applications, this
problem is extensively studied in recent years. Among the various approaches to
the problem, attention guided progressive generation is shown to produce
state-of-the-art results in most cases. In this paper, we present an improved
network architecture for pose transfer by introducing attention links at every
resolution level of the encoder and decoder. By utilizing such dense
multi-scale attention guided approach, we are able to achieve significant
improvement over the existing methods both visually and analytically. We
conclude our findings with extensive qualitative and quantitative comparisons
against several existing methods on the DeepFashion dataset.
