VITON-GAN: Virtual Try-on Image Generator Trained with Adversarial Loss

dc.contributor.authorHonda, Shionen_US
dc.contributor.editorFusiello, Andrea and Bimber, Oliveren_US
dc.date.accessioned2019-05-05T17:47:59Z
dc.date.available2019-05-05T17:47:59Z
dc.date.issued2019
dc.description.abstractGenerating a virtual try-on image from in-shop clothing images and a model person's snapshot is a challenging task because the human body and clothes have high flexibility in their shapes. In this paper, we develop a Virtual Try-on Generative Adversarial Network (VITON-GAN), that generates virtual try-on images using images of in-shop clothing and a model person. This method enhances the quality of the generated image when occlusion is present in a model person's image (e.g., arms crossed in front of the clothes) by adding an adversarial mechanism in the training pipeline.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEurographics 2019 - Posters
dc.identifier.doi10.2312/egp.20191043
dc.identifier.issn1017-4656
dc.identifier.pages9-10
dc.identifier.urihttps://doi.org/10.2312/egp.20191043
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egp20191043
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectImage representations
dc.subjectApplied computing
dc.subjectOnline shopping
dc.titleVITON-GAN: Virtual Try-on Image Generator Trained with Adversarial Lossen_US
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