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Multi-domain image-to-image translation has gained increasing attentionrecently. Previous methods take an image and some target attributes as inputsand generate an output image with the desired attributes. However, such methodshave two limitations. First, these methods assume binary-valued attributes andthus cannot yield satisfactory results for fine-grained control. Second, thesemethods require specifying the entire set of target attributes, even if most ofthe attributes would not be changed. To address these limitations, we proposeRelGAN, a new method for multi-domain image-to-image translation. The key ideais to use relative attributes, which describes the desired change on selectedattributes. Our method is capable of modifying images by changing particularattributes of interest in a continuous manner while preserving the otherattributes. Experimental results demonstrate both the quantitative andqualitative effectiveness of our method on the tasks of facial attributetransfer and interpolation.

RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes