  Recommendation in the fashion domain has seen a recent surge in research in
various areas, for example, shop-the-look, context-aware outfit creation,
personalizing outfit creation, etc. The majority of state of the art approaches
in the domain of outfit recommendation pursue to improve compatibility among
items so as to produce high quality outfits. Some recent works have realized
that style is an important factor in fashion and have incorporated it in
compatibility learning and outfit generation. These methods often depend on the
availability of fine-grained product categories or the presence of rich item
attributes (e.g., long-skirt, mini-skirt, etc.). In this work, we aim to
generate outfits conditional on styles or themes as one would dress in real
life, operating under the practical assumption that each item is mapped to a
high level category as driven by the taxonomy of an online portal, like
outdoor, formal etc and an image. We use a novel style encoder network that
renders outfit styles in a smooth latent space. We present an extensive
analysis of different aspects of our method and demonstrate its superiority
over existing state of the art baselines through rigorous experiments.
