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The task of unsupervised image-to-image translation has seen substantialadvancements in recent years through the use of deep neural networks.Typically, the proposed solutions learn the characterizing distribution of twolarge, unpaired collections of images, and are able to alter the appearance ofa given image, while keeping its geometry intact. In this paper, we explore thecapabilities of neural networks to understand image structure given only asingle pair of images, A and B. We seek to generate images that arestructurally aligned: that is, to generate an image that keeps the appearanceand style of B, but has a structural arrangement that corresponds to A. The keyidea is to map between image patches at different scales. This enablescontrolling the granularity at which analogies are produced, which determinesthe conceptual distinction between style and content. In addition to structuralalignment, our method can be used to generate high quality imagery in otherconditional generation tasks utilizing images A and B only: guided imagesynthesis, style and texture transfer, text translation as well as videotranslation. Our code and additional results are available inhttps://github.com/rmokady/structural-analogy/.

Structural Analogy from a Single Image Pair