  Multi-domain image-to-image translation has gained increasing attention
recently. Previous methods take an image and some target attributes as inputs
and generate an output image with the desired attributes. However, such methods
have two limitations. First, these methods assume binary-valued attributes and
thus cannot yield satisfactory results for fine-grained control. Second, these
methods require specifying the entire set of target attributes, even if most of
the attributes would not be changed. To address these limitations, we propose
RelGAN, a new method for multi-domain image-to-image translation. The key idea
is to use relative attributes, which describes the desired change on selected
attributes. Our method is capable of modifying images by changing particular
attributes of interest in a continuous manner while preserving the other
attributes. Experimental results demonstrate both the quantitative and
qualitative effectiveness of our method on the tasks of facial attribute
transfer and interpolation.
