Abstract
We propose to automatically create capsule wardrobes.
Given an inventory of candidate garments and accessories,
the algorithm must assemble a minimal set of items that provides maximal mix-and-match outfits. We pose the task as
a subset selection problem. To permit efficient subset selection over the space of all outfit combinations, we develop
submodular objective functions capturing the key ingredients of visual compatibility, versatility, and user-specific
preference. Since adding garments to a capsule only expands its possible outfits, we devise an iterative approach
to allow near-optimal submodular function maximization.
Finally, we present an unsupervised approach to learn visual compatibility from “in the wild” full body outfit photos; the compatibility metric translates well to cleaner catalog photos and improves over existing methods. Our results on thousands of pieces from popular fashion websites
show that automatic capsule creation has potential to mimic
skilled fashionistas in assembling flexible wardrobes, while
being significantly more scalable