Shape Controllable Virtual Try-on for Underwear Models

TL;DR

This paper introduces a shape controllable virtual try-on network for underwear models that allows precise control over clothing shape and size, producing high-resolution, textured images, addressing limitations of existing 2D methods.

Contribution

The paper proposes SC-VTON, a novel network integrating graph attention, control points, and specialized networks to enable accurate shape control and high-quality virtual try-on for underwear models.

Findings

Achieves precise shape control in virtual try-on.

Generates high-resolution, detailed textured images.

Outperforms existing methods in accuracy and quality.

Abstract

Image virtual try-on task has abundant applications and has become a hot research topic recently. Existing 2D image-based virtual try-on methods aim to transfer a target clothing image onto a reference person, which has two main disadvantages: cannot control the size and length precisely; unable to accurately estimate the user's figure in the case of users wearing thick clothes, resulting in inaccurate dressing effect. In this paper, we put forward an akin task that aims to dress clothing for underwear models. %, which is also an urgent need in e-commerce scenarios. To solve the above drawbacks, we propose a Shape Controllable Virtual Try-On Network (SC-VTON), where a graph attention network integrates the information of model and clothing to generate the warped clothing image. In addition, the control points are incorporated into SC-VTON for the desired clothing shape. Furthermore, by adding a Splitting Network and a Synthesis Network, we can use clothing/model pair data to help optimize the deformation module and generalize the task to the typical virtual try-on task. Extensive experiments show that the proposed method can achieve accurate shape control. Meanwhile, compared with other methods, our method can generate high-resolution results with detailed textures.