The challenge of building good outfits out of the individual garments in a wardrobe may leave consumers with seemingly too few options despite the numerous pieces and combinations in their wardrobes.
A system that could identify good options for a user's physical attributes and preferences from all the outfit permutations possible in their wardrobe would alleviate the cognitive bottleneck of deciding what to wear, while also helping to reduce consumption by providing consumers with “new” outfits built from pre-existing garments. To do this, we must first articulate the relationship between garments and bodies and successful outfits. This research explores the question of how to describe, model, and predict the body- garment- and outfit-level attributes that contribute to user satisfaction with an outfit.
Funded by UMN Informatics Institute and National Science Foundation (grant CHS-1715200)
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