ClothFit: Cloth-Human-Attribute Guided Virtual Try-On Network Using 3D Simulated Dataset

• URL: http://arxiv.org/abs/2306.13908v1
• Date: Sat, 24 Jun 2023 08:57:36 GMT
• Title: ClothFit: Cloth-Human-Attribute Guided Virtual Try-On Network Using 3D Simulated Dataset
• Authors: Yunmin Cho, Lala Shakti Swarup Ray, Kundan Sai Prabhu Thota, Sungho Suh, Paul Lukowicz
• Abstract summary: We propose a novel virtual try-on method called ClothFit. It can predict the draping shape of a garment on a target body based on the actual size of the garment and human attributes. Our experimental results demonstrate that ClothFit can significantly improve the existing state-of-the-art methods in terms of photo-realistic virtual try-on results.
• Score: 5.260305201345232
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online clothing shopping has become increasingly popular, but the high rate of returns due to size and fit issues has remained a major challenge. To address this problem, virtual try-on systems have been developed to provide customers with a more realistic and personalized way to try on clothing. In this paper, we propose a novel virtual try-on method called ClothFit, which can predict the draping shape of a garment on a target body based on the actual size of the garment and human attributes. Unlike existing try-on models, ClothFit considers the actual body proportions of the person and available cloth sizes for clothing virtualization, making it more appropriate for current online apparel outlets. The proposed method utilizes a U-Net-based network architecture that incorporates cloth and human attributes to guide the realistic virtual try-on synthesis. Specifically, we extract features from a cloth image using an auto-encoder and combine them with features from the user's height, weight, and cloth size. The features are concatenated with the features from the U-Net encoder, and the U-Net decoder synthesizes the final virtual try-on image. Our experimental results demonstrate that ClothFit can significantly improve the existing state-of-the-art methods in terms of photo-realistic virtual try-on results.

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