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Object Transfiguration replaces an object in an image with another objectfrom a second image. For example it can perform tasks like "putting exactlythose eyeglasses from image A on the nose of the person in image B". Usage ofexemplar images allows more precise specification of desired modifications andimproves the diversity of conditional image generation. However, previousmethods that rely on feature space operations, require paired data and/orappearance models for training or disentangling objects from background. Inthis work, we propose a model that can learn object transfiguration from twounpaired sets of images: one set containing images that "have" that kind ofobject, and the other set being the opposite, with the mild constraint that theobjects be located approximately at the same place. For example, the trainingdata can be one set of reference face images that have eyeglasses, and anotherset of images that have not, both of which spatially aligned by face landmarks.Despite the weak 0/1 labels, our model can learn an "eyeglasses" subspace thatcontain multiple representatives of different types of glasses. Consequently,we can perform fine-grained control of generated images, like swapping theglasses in two images by swapping the projected components in the "eyeglasses"subspace, to create novel images of people wearing eyeglasses.  Overall, our deterministic generative model learns disentangled attributesubspaces from weakly labeled data by adversarial training. Experiments onCelebA and Multi-PIE datasets validate the effectiveness of the proposed modelon real world data, in generating images with specified eyeglasses, smiling,hair styles, and lighting conditions etc. The code is available online.

GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data