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arXiv · 2212.04437

C-VTON: Context-Driven Image-Based Virtual Try-On Network

Benjamin Fele·Ajda Lampe·Peter Peer·Vitomir Štruc
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Abstract

Image-based virtual try-on techniques have shown great promise for enhancing the user-experience and improving customer satisfaction on fashion-oriented e-commerce platforms. However, existing techniques are currently still limited in the quality of the try-on results they are able to produce from input images of diverse characteristics. In this work, we propose a Context-Driven Virtual Try-On Network (C-VTON) that addresses these limitations and convincingly transfers selected clothing items to the target subjects even under challenging pose configurations and in the presence of self-occlusions. At the core of the C-VTON pipeline are: (i) a geometric matching procedure that efficiently aligns the target clothing with the pose of the person in the input images, and (ii) a powerful image generator that utilizes various types of contextual information when synthesizing the final try-on result. C-VTON is evaluated in rigorous experiments on the VITON and MPV datasets and in comparison to state-of-the-art techniques from the literature. Experimental results show that the proposed approach is able to produce photo-realistic and visually convincing results and significantly improves on the existing state-of-the-art.

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Benjamin Fele, Ajda Lampe, Peter Peer, Vitomir Štruc. 2022-12-08. C-VTON: Context-Driven Image-Based Virtual Try-On Network. https://doi.org/10.1109/wacv51458.2022.00226

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