1 research outputs found
Coherent and Controllable Outfit Generation
When thinking about dressing oneself, people often have a theme in mind
whether they're going to a tropical getaway or wish to appear attractive at a
cocktail party. A useful outfit generation system should come up with clothing
items that are compatible while matching a theme specified by the user.
Existing methods use item-wise compatibility between products but lack an
effective way to enforce a global constraint (e.g., style, occasion).
We introduce a method that generates outfits whose items match a theme
described by a text query. Our method uses text and image embeddings to
represent fashion items. We learn a multimodal embedding where the image
representation for an item is close to its text representation, and use this
embedding to measure item-query coherence. We then use a discriminator to
compute compatibility between fashion items. This strategy yields a
compatibility prediction method that meets or exceeds the state of the art.
Our method combines item-item compatibility and item-query coherence to
construct an outfit whose items are (a) close to the query and (b) compatible
with one another. Quantitative evaluation shows that the items in our outfits
are tightly clustered compared to standard outfits. Furthermore, outfits
produced by similar queries are close to one another, and outfits produced by
very different queries are far apart. Qualitative evaluation shows that our
method responds well to queries. A user study suggests that people understand
the match between the queries and the outfits produced by our method