  Pose guided synthesis aims to generate a new image in an arbitrary target
pose while preserving the appearance details from the source image. Existing
approaches rely on either hard-coded spatial transformations or 3D body
modeling. They often overlook complex non-rigid pose deformation or unmatched
occluded regions, thus fail to effectively preserve appearance information. In
this paper, we propose an unsupervised pose flow learning scheme that learns to
transfer the appearance details from the source image. Based on such learned
pose flow, we proposed GarmentNet and SynthesisNet, both of which use
multi-scale feature-domain alignment for coarse-to-fine synthesis. Experiments
on the DeepFashion, MVC dataset and additional real-world datasets demonstrate
that our approach compares favorably with the state-of-the-art methods and
generalizes to unseen poses and clothing styles.
