From 2D to 3D geodesic-based garment matching

Egils Avots, Meysam Madadi, Sergio Escalera, Jordi Gonzàlez, Xavier Baro, Paul Pällin, Gholamreza Anbarjafari

Research output: Contribution to journalArticleResearch

Abstract

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. A new approach for 2D to 3D garment retexturing is proposed based on Gaussian mixture models and thin plate splines (TPS). An automatically segmented garment of an individual is matched to a new source garment and rendered, resulting in augmented images in which the target garment has been retextured using the texture of the source garment. We divide the problem into garment boundary matching based on Gaussian mixture models and then interpolate inner points using surface topology extracted through geodesic paths, which leads to a more realistic result than standard approaches. We evaluated and compared our system quantitatively by root mean square error (RMS) and qualitatively using the mean opinion score (MOS), showing the benefits of the proposed methodology on our gathered dataset.
Original languageEnglish
Pages (from-to)25829-25853
JournalMultimedia Tools and Applications
Volume78
DOIs
Publication statusPublished - 30 Sep 2019

Keywords

  • Gaussian mixture model
  • Geodesic distance
  • RGBD image processing
  • Shape matching
  • Texture mapping

Fingerprint

Dive into the research topics of 'From 2D to 3D geodesic-based garment matching'. Together they form a unique fingerprint.

Cite this