Virtual Garment Fitting Through Parsing and Context-Aware Generative Adversarial Networks with Discriminator Group

Wei Hong Su, Sze Ann Chen, Chen I. Chin, Hsu Feng Hsiao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Owing to the rapid growth of the e-commerce industry, image-based virtual try-on has emerged as a popular research topic in recent years. Despite the introduction of multiple approaches to achieve this concept, there remains ample scope for research and improvement. In this regard, Generative Adversarial Networks (GANs) demonstrate a framework possessing immense potential for subsequent development. Nonetheless, the generated images reported in the literature often manifest blurred edges between semantic regions, thereby diminishing the credibility of results. Furthermore, the generation of try-on images may retain the original shape of the upper body clothing on the model mistakenly, such as the length and tightness of the torso, rather than adapting to the shape of the target clothing. In this paper, we propose a more comprehensive architecture to overcome the limitations of GAN-based approaches, which includes the following contributions. First, we introduce a new parsing and context generator that takes into account the warped binary mask of the geometric matching image of the target clothing. The outputs of this generator incorporate the generation of human parsing images that correspond to the generated try-on images. Moreover, we have designed a novel discriminator group that is specifically focused on judging whether the generated image is a reasonable representation of the specific clothing being worn. According to the experimental results, our method effectively exhibits better synthesis quality and remedies the common challenges encountered while using GANs for virtual try-on.

Original languageEnglish
Title of host publication2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1732-1738
Number of pages7
ISBN (Electronic)9798350300673
DOIs
StatePublished - 2023
Event2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 - Taipei, Taiwan
Duration: 31 Oct 20233 Nov 2023

Publication series

Name2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023

Conference

Conference2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
Country/TerritoryTaiwan
CityTaipei
Period31/10/233/11/23

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