Authors:
Danyal Mahmood
;
Wei Leong
;
Humaira Nisar
and
Ahmad Mazlan
Affiliation:
Department of Electronic Engineering, Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia
Keyword(s):
Virtual Try-On, Virtual Fitting Room, Deep Generative Models, Geometric Matching, Depth Estimation.
Abstract:
With the rise in digital technology and the fast pace of life, as well as the change in lifestyle due to the pandemic, people have started adopting online shopping in the garment industry as well. Hence, research on Virtual Try-On (VTO) technologies to be implemented in virtual fitting rooms (VFRs) has drawn significant attention. The existing VFR technologies rely on deep generative models with an end-to-end pipeline, from feature extraction to garment warping and refinement. While currently there are 2D and 3D VTO solutions, the 3D ones have enormous commercial potential in the fashion market as the technology has been proven effective for providing a photo-realistic and detailed try-on result. However, the existing 3D VTO solutions principally rely on annotated human body shapes or avatars, which are unrealistic. By integrating the technologies embedded in both 2D and 3D VTO solutions, this paper proposes a VTO solution that relies on geometric settings in the 3D space namely the
3D Virtual Fitting Network (3D VFN), that solely relies on 2D RGB garment and single-person human images as inputs, generating a photo-realistic warped garment output image by utilizing the geometric settings in the 3D space.
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