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• W4312462476 abstract "Image-based virtual try-on strives to transfer the appearance of a clothing item onto the image of a target person. Prior work focuses mainly on upper-body clothes (e.g. t-shirts, shirts, and tops) and neglects full-body or lower-body items. This shortcoming arises from a main factor: current publicly available datasets for image-based virtual try-on do not account for this variety, thus limiting progress in the field. To address this deficiency, we introduce Dress Code, which contains images of multi-category clothes. Dress Code is more than $$3times $$ larger than publicly available datasets for image-based virtual try-on and features high-resolution paired images ( $$1024 times 768$$ ) with front-view, full-body reference models. To generate HD try-on images with high visual quality and rich in details, we propose to learn fine-grained discriminating features. Specifically, we leverage a semantic-aware discriminator that makes predictions at pixel-level instead of image- or patch-level. Extensive experimental evaluation demonstrates that the proposed approach surpasses the baselines and state-of-the-art competitors in terms of visual quality and quantitative results. The Dress Code dataset is publicly available at https://github.com/aimagelab/dress-code ." @default.
• W4312462476 created "2023-01-04" @default.
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• W4312462476 date "2022-01-01" @default.
• W4312462476 modified "2023-10-17" @default.
• W4312462476 title "Dress Code: High-Resolution Multi-category Virtual Try-On" @default.
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• W4312462476 doi "https://doi.org/10.1007/978-3-031-20074-8_20" @default.
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