DressUp!: outfit synthesis through automatic optimization

  • Authors:
  • Lap-Fai Yu;Sai-Kit Yeung;Demetri Terzopoulos;Tony F. Chan

  • Affiliations:
  • University of California, Los Angeles;Singapore University of Technology and Design;University of California, Los Angeles;Hong Kong University of Science and Technology

  • Venue:
  • ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

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.