  Attribute image manipulation has been a very active topic since the
introduction of Generative Adversarial Networks (GANs). Exploring the
disentangled attribute space within a transformation is a very challenging task
due to the multiple and mutually-inclusive nature of the facial images, where
different labels (eyeglasses, hats, hair, identity, etc.) can co-exist at the
same time. Several works address this issue either by exploiting the modality
of each domain/attribute using a conditional random vector noise, or extracting
the modality from an exemplary image. However, existing methods cannot handle
both random and reference transformations for multiple attributes, which limits
the generality of the solutions. In this paper, we successfully exploit a
multimodal representation that handles all attributes, be it guided by random
noise or exemplar images, while only using the underlying domain information of
the target domain. We present extensive qualitative and quantitative results
for facial datasets and several different attributes that show the superiority
of our method. Additionally, our method is capable of adding, removing or
changing either fine-grained or coarse attributes by using an image as a
reference or by exploring the style distribution space, and it can be easily
extended to head-swapping and face-reenactment applications without being
trained on videos.
