DressUp! Outfit synthesis through automatic optimization

Lap Fai Yu*, Sai Kit Yeung, Demetri Terzopoulos, Tony Chan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

We present an automatic optimization approach to outfit synthesis. Given the hair color, eye color, and skin color of the input body, plus a wardrobe of clothing items, our outfit synthesis system suggests a set of outfits subject to a particular dress code. We introduce a probabilistic framework for modeling and applying dress codes that exploits a Bayesian network trained on example images of real-world outfits. Suitable outfits are then obtained by optimizing a cost function that guides the selection of clothing items to maximize the color compatibility and dress code suitability. We demonstrate our approach on the four most common dress codes: Casual, Sportswear, Business-Casual, and Business. A perceptual study validated on multiple resultant outfits demonstrates the efficacy of our framework.

Original languageEnglish (US)
Article number134
JournalACM Transactions on Graphics
Volume31
Issue number6
DOIs
StatePublished - Nov 1 2012

Keywords

  • Clothing combination
  • Color matching
  • Fashion
  • Functionally realistic
  • Optimization
  • Perception
  • Procedural modeling
  • Variety
  • Virtual world modeling

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

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