# VTON-IT: Virtual Try-On using Image Translation Source: [VTON-IT: Virtual Try-On using Image Translation]() Collection index: [llms.txt]() Data updated: 2026-01-02T05:50:41.043Z Authors: Santosh Adhikari, Bishnu Bhusal, Prashant Ghimire, Anil Shrestha Published: 2023-10-06T19:47:20.000Z ## Abstract Virtual Try-On (trying clothes virtually) is a promising application of the Generative Adversarial Network (GAN). However, it is an arduous task to transfer the desired clothing item onto the corresponding regions of a human body because of varying body size, pose, and occlusions like hair and overlapped clothes. In this paper, we try to produce photo-realistic translated images through semantic segmentation and a generative adversarial architecture-based image translation network. We present a novel image-based Virtual Try-On application VTON-IT that takes an RGB image, segments desired body part, and overlays target cloth over the segmented body region. Most state-of-the-art GAN-based Virtual Try-On applications produce unaligned pixelated synthesis images on real-life test images. However, our approach generates high-resolution natural images with detailed textures on such variant images. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Virtual Try-On on FGC6](): Virtual Try-On; FGC6; SSIM: 0.93. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()