Creating Capsule Wardrobes from Fashion Images
This addresses the need for efficient and personalized wardrobe assembly for fashion consumers, though it is incremental in applying existing optimization techniques to a new domain.
The paper tackles the problem of automatically creating capsule wardrobes by selecting a minimal set of fashion items to maximize mix-and-match outfits, using submodular optimization and unsupervised learning for visual compatibility, with results showing scalability and potential to mimic skilled fashionistas.
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.