@Article{EgilsAvots2019,
author="Egils Avots
and Meysam Madadi
and Sergio Escalera
and Jordi Gonzalez
and Xavier Baro
and Paul Pallin
and Gholamreza Anbarjafari",
title="From 2D to 3D geodesic-based garment matching",
journal="Multimedia Tools and Applications",
year="2019",
volume="78",
number="18",
pages="25829--25853",
optkeywords="Shape matching",
optkeywords="Geodesic distance",
optkeywords="Texture mapping",
optkeywords="RGBD image processing",
optkeywords="Gaussian mixture model",
abstract="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.",
optnote="HuPBA; ISE; 600.098; 600.119; 602.133",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3317), last updated on Tue, 25 Jan 2022 11:46:42 +0100",
doi="10.1007/s11042-019-7739-5",
opturl="https://link.springer.com/article/10.1007/s11042-019-7739-5",
file=":http://refbase.cvc.uab.es/files/AME2019.pdf:PDF"
}