LC-VTON: Length Controllable Virtual Try-on Network

Abstract

Image-based virtual try-on provides customers with convenient online clothes selections by transferring garments onto a reference person. Despite the emergence of several solutions to generate photo-realistic images and adapt to complex poses, controlling clothing length remains a challenge. We argue that the clothing reconstruction did not consider clothing length information, which results in clothing length being uncontrollable in most virtual try-on methods. To overcome this limitation, a novel clothing-agnostic person representation is proposed, which eliminates clothing information and quantifies clothing length as a numerical value. A new segmentation generator is designed to predict try-on segmentation maps of any length conditioned on this representation. Moreover, we correct two inaccurate labels, which enables our model to utilize clothing length control to generate a wider range of garment interactions in images, such as the top tucked into or worn over the bottom, as well as the top and bottom worn separately without intersecting. Extensive experiments demonstrate that our method achieves the goal of continuous clothing length control and generates photo-realistic images with fine details that outperform most baseline methods in terms of quantitative and qualitative metrics.

Keywords

• Virtual try-on
• clothing length controllable
• conditional semantic generation
• generative adversarial network