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

TL;DR

C-VTON is a novel virtual try-on network that effectively transfers clothing onto people in images, handling challenging poses and occlusions to produce realistic results, surpassing current methods in quality.

Contribution

The paper introduces a context-driven approach with geometric matching and advanced image synthesis to improve virtual try-on quality under complex conditions.

Findings

Outperforms state-of-the-art methods in visual quality

Handles challenging poses and self-occlusions effectively

Produces photo-realistic try-on results

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.