  In this paper, we develop a robust 3D garment digitization solution that can
generalize well on real-world fashion catalog images with cloth texture
occlusions and large body pose variations. We assumed fixed topology parametric
template mesh models for known types of garments (e.g., T-shirts, Trousers) and
perform mapping of high-quality texture from an input catalog image to UV map
panels corresponding to the parametric mesh model of the garment. We achieve
this by first predicting a sparse set of 2D landmarks on the boundary of the
garments. Subsequently, we use these landmarks to perform
Thin-Plate-Spline-based texture transfer on UV map panels. Subsequently, we
employ a deep texture inpainting network to fill the large holes (due to view
variations & self-occlusions) in TPS output to generate consistent UV maps.
Furthermore, to train the supervised deep networks for landmark prediction &
texture inpainting tasks, we generated a large set of synthetic data with
varying texture and lighting imaged from various views with the human present
in a wide variety of poses. Additionally, we manually annotated a small set of
fashion catalog images crawled from online fashion e-commerce platforms to
finetune. We conduct thorough empirical evaluations and show impressive
qualitative results of our proposed 3D garment texture solution on fashion
catalog images. Such 3D garment digitization helps us solve the challenging
task of enabling 3D Virtual Try-on.
