Image-based virtual try-on strives to transfer the appearance of a clothing item onto the image of a target person. Existing literature 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. In this research activity, we introduce Dress Code, a novel dataset which contains images of multi-category clothes. Dress Code is more than 3x larger than publicly available datasets for image-based virtual try-on and features high-resolution paired images (1024 x 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. The Dress Code dataset is publicly available at https://github.com/aimagelab/dress-code.

Dress Code: High-Resolution Multi-Category Virtual Try-On / Morelli, Davide; Fincato, Matteo; Cornia, Marcella; Landi, Federico; Cesari, Fabio; Cucchiara, Rita. - (2022). (Intervento presentato al convegno IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops tenutosi a New Orleans, Louisiana nel June 19-24, 2022) [10.1109/CVPRW56347.2022.00243].

Dress Code: High-Resolution Multi-Category Virtual Try-On

Davide Morelli;Matteo Fincato;Marcella Cornia;Federico Landi;Rita Cucchiara
2022

Abstract

Image-based virtual try-on strives to transfer the appearance of a clothing item onto the image of a target person. Existing literature 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. In this research activity, we introduce Dress Code, a novel dataset which contains images of multi-category clothes. Dress Code is more than 3x larger than publicly available datasets for image-based virtual try-on and features high-resolution paired images (1024 x 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. The Dress Code dataset is publicly available at https://github.com/aimagelab/dress-code.
2022
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
New Orleans, Louisiana
June 19-24, 2022
Morelli, Davide; Fincato, Matteo; Cornia, Marcella; Landi, Federico; Cesari, Fabio; Cucchiara, Rita
Dress Code: High-Resolution Multi-Category Virtual Try-On / Morelli, Davide; Fincato, Matteo; Cornia, Marcella; Landi, Federico; Cesari, Fabio; Cucchiara, Rita. - (2022). (Intervento presentato al convegno IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops tenutosi a New Orleans, Louisiana nel June 19-24, 2022) [10.1109/CVPRW56347.2022.00243].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1272638
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