@inproceedings{bb54030f60f94e3191e644abf63f8031,
title = "M-VTON: Multi-layer Virtual Try-on System",
abstract = "Fashion recommendation is an issue of considerable importance in the fashion ecommerce industry. The essential part of the recommendation task is how to represent outfits. We propose a multi-layer try-on system (M-VTON) based on deep learning methods. M-VTON can generates vivid try-on images to represent an outfit using its separate product images. It includes a fashion keypoint detection model and a semantic segmentation model. The detection model aims to estimate the keypoints of garments in order to calculate an item{\textquoteright}s scale and position. The segmentation model serves to separate garments into front and back pieces, which are used to generate the multi-layers of outfits. We conducted experiments on two mainstream fashion datasets and results support the effectiveness of our proposed approach.",
keywords = "fashion compatibility learning, Fashion recommendation, keypoint detection, semantic segmentation, virtual try-on",
author = "Kaicheng Pang and Xingxing Zou and Fangjian Liao and Waikeung Wong",
note = "Publisher Copyright: {\textcopyright} 2023, The Hong Kong Polytechnic University. All rights reserved.; 12th International Conference on Design and Semantics of Form and Movement, DeSForM 2023 ; Conference date: 05-07-2023 Through 07-07-2023",
year = "2023",
language = "English",
isbn = "9789623678704",
series = "International Conference on Design and Semantics of Form and Movement",
publisher = "The Hong Kong Polytechnic University",
pages = "94--104",
editor = "Miguel Bruns and Lin-Lin Chen and Jun Hu and Sara Colombo and Yihyun Lim and Steven Kyffin and Ozcan Vieira and Raijmakers, \{E. Jeroen\} and Lucia Rampino and Ramirez, \{Edgar Rodriguez\} and Steffen, \{Dagmar Johanna\} and Calvin Wong",
booktitle = "International Conference on Design and Semantics of Form and Movement",
address = "Hong Kong",
}