  In this paper, we examine the visual variability of objects across different
ad categories, i.e. what causes an advertisement to be visually persuasive. We
focus on modeling and generating faces which appear to come from different
types of ads. For example, if faces in beauty ads tend to be women wearing
lipstick, a generative model should portray this distinct visual appearance.
Training generative models which capture such category-specific differences is
challenging because of the highly diverse appearance of faces in ads and the
relatively limited amount of available training data. To address these
problems, we propose a conditional variational autoencoder which makes use of
predicted semantic attributes and facial expressions as a supervisory signal
when training. We show how our model can be used to produce visually distinct
faces which appear to be from a fixed ad topic category. Our human studies and
quantitative and qualitative experiments confirm that our method greatly
outperforms a variety of baselines, including two variations of a
state-of-the-art generative adversarial network, for transforming faces to be
more ad-category appropriate. Finally, we show preliminary generation results
for other types of objects, conditioned on an ad topic.
