  We address the problem of unpaired geometric image-to-image translation.
Rather than transferring the style of an image as a whole, our goal is to
translate the geometry of an object as depicted in different domains while
preserving its appearance characteristics. Our model is trained in an unpaired
fashion, i.e. without the need of paired images during training. It performs
all steps of the shape transfer within a single model and without additional
post-processing stages. Extensive experiments on the VITON, CMU-Multi-PIE and
our own FashionStyle datasets show the effectiveness of the method. In
addition, we show that despite their low-dimensionality, the features learned
by our model are useful to the item retrieval task.
