From 2D to 3D Geodesic-based Garment Matching
This work addresses garment retexturing for augmented reality or fashion applications, but it appears incremental as it builds on existing methods with specific enhancements.
The paper tackled the problem of 2D to 3D garment retexturing by matching garments using Gaussian mixture models and thin plate splines, resulting in augmented images with improved realism, as shown by lower mean square error and higher mean opinion scores compared to standard approaches.
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 by 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 mean square error (MSE) and qualitatively using the mean opinion score (MOS), showing the benefits of the proposed methodology on our gathered dataset.