  Generative adversarial networks offer the possibility to generate deceptively
real images that are almost indistinguishable from actual photographs. Such
systems however rely on the presence of large datasets to realistically
replicate the corresponding domain. This is especially a problem if not only
random new images are to be generated, but specific (continuous) features are
to be co-modeled. A particularly important use case in \emph{Human-Computer
Interaction} (HCI) research is the generation of emotional images of human
faces, which can be used for various use cases, such as the automatic
generation of avatars. The problem hereby lies in the availability of training
data. Most suitable datasets for this task rely on categorical emotion models
and therefore feature only discrete annotation labels. This greatly hinders the
learning and modeling of smooth transitions between displayed affective states.
To overcome this challenge, we explore the potential of label interpolation to
enhance networks trained on categorical datasets with the ability to generate
images conditioned on continuous features.
