Name
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 \(3\times \) 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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-20074-8_20 | European Conference on Computer Vision |
Keywords | DocType | Citations |
Dress code dataset,Virtual try-on,Image synthesis | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Morelli Davide | 1 | 0 | 0.34 |
| Fincato Matteo | 2 | 0 | 0.34 |
| Cornia Marcella | 3 | 0 | 0.34 |
| Landi Federico | 4 | 0 | 0.34 |
| Cesari Fabio | 5 | 0 | 0.34 |
| Cucchiara Rita | 6 | 0 | 0.34 |