Garment retexturing using Kinect V2.0
Date
2017
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
This thesis describes three new garment retexturing methods for FitsMe virtual fitting room applications
using data from Microsoft Kinect II RGB-D camera.
The first method, which is introduced, is an automatic technique for garment retexturing using
a single RGB-D image and infrared information obtained from Kinect II. First, the garment
is segmented out from the image using GrabCut or depth segmentation. Then texture domain
coordinates are computed for each pixel belonging to the garment using normalized 3D information.
Afterwards, shading is applied to the new colors from the texture image.
The second method proposed in this work is about 2D to 3D garment retexturing where a segmented
garment of a manikin or person is matched to a new source garment and retextured,
resulting in augmented images in which the new source garment is transferred to the manikin
or person. The problem is divided into garment boundary matching based on point set registration
which uses 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.
The final contribution of this thesis is by introducing another novel method which is used for
increasing the texture quality of a 3D model of a garment, by using the same Kinect frame
sequence which was used in the model creation. Firstly, a structured mesh must be created
from the 3D model, therefore the 3D model is wrapped to a base model with defined seams and
texture map. Afterwards frames are matched to the newly created model and by process of ray
casting the color values of the Kinect frames are mapped to the UV map of the 3D model.
Description
Keywords
Kinect, Infrared Image, Garment matching, Garment Retexturing, Texture Mapping