Cloth Interactive Transformer for Virtual Try-On

TL;DR

This paper introduces a novel cloth interactive transformer (CIT) framework for virtual try-on, effectively capturing long-range correlations and mutual dependencies to produce more realistic and natural-looking try-on results.

Contribution

The paper proposes a two-stage transformer-based approach that models cloth-person interactions and mutual dependencies, improving virtual try-on realism over CNN-based methods.

Findings

CIT achieves competitive performance on public fashion datasets.

The method produces more natural and consistent try-on results.

Long-range correlation modeling enhances virtual try-on quality.

Abstract

The 2D image-based virtual try-on has aroused increased interest from the multimedia and computer vision fields due to its enormous commercial value. Nevertheless, most existing image-based virtual try-on approaches directly combine the person-identity representation and the in-shop clothing items without taking their mutual correlations into consideration. Moreover, these methods are commonly established on pure convolutional neural networks (CNNs) architectures which are not simple to capture the long-range correlations among the input pixels. As a result, it generally results in inconsistent results. To alleviate these issues, in this paper, we propose a novel two-stage cloth interactive transformer (CIT) method for the virtual try-on task. During the first stage, we design a CIT matching block, aiming to precisely capture the long-range correlations between the cloth-agnostic person information and the in-shop cloth information. Consequently, it makes the warped in-shop clothing items look more natural in appearance. In the second stage, we put forth a CIT reasoning block for establishing global mutual interactive dependencies among person representation, the warped clothing item, and the corresponding warped cloth mask. The empirical results, based on mutual dependencies, demonstrate that the final try-on results are more realistic. Substantial empirical results on a public fashion dataset illustrate that the suggested CIT attains competitive virtual try-on performance.