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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 totranslate the geometry of an object as depicted in different domains whilepreserving its appearance characteristics. Our model is trained in an unpairedfashion, i.e. without the need of paired images during training. It performsall steps of the shape transfer within a single model and without additionalpost-processing stages. Extensive experiments on the VITON, CMU-Multi-PIE andour own FashionStyle datasets show the effectiveness of the method. Inaddition, we show that despite their low-dimensionality, the features learnedby our model are useful to the item retrieval task.

An Unpaired Shape Transforming Method for Image Translation and Cross-Domain Retrieval