Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
High-resolution virtual try-on with garment extraction using generative adversarial networks
Image-based virtual try-on aims to depict an individual wearing a garment not originally worn by them. While existing literature predominantly focuses on garments from standalone images, this research addresses the use of images where the garment is already being worn by another individual. The stud...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Statistical Sciences
2025
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613183953338368 |
|---|---|
| access_status_str | Open Access |
| author | Charters, Daniel J |
| author2 | Britz, Stefan S |
| author_browse | Britz, Stefan S Charters, Daniel J |
| author_facet | Britz, Stefan S Charters, Daniel J |
| author_sort | Charters, Daniel J |
| collection | Thesis |
| description | Image-based virtual try-on aims to depict an individual wearing a garment not originally worn by them. While existing literature predominantly focuses on garments from standalone images, this research addresses the use of images where the garment is already being worn by another individual. The study bridges a notable gap as most current systems are tailored for standalone garment images. The proposed system, given a pair of high-resolution images, extracts the garment from one, refines it using context-aware image inpainting, and subsequently transfers it onto the second image's subject. The methodology incorporates various off-the-shelf models, notably Part Grouping Network (PGN), Densepose, and OpenPose for pre-processing. A state-of-the-art context-aware inpainting model refines the garments, and the final synthesis leverages the HR-VITON architecture, producing images at a resolution of 768 × 1024. Distinctively, our model processes both standalone and garment-on-person images. Evaluating the models involves testing on 2 032 high-resolution images under both paired and unpaired conditions. Metrics such as RMSE, Peak Signal-to-Noise Ratio (PSNR), Learned Perceptual Image Patch Similarity (LPIPS), Structural Similarity (SSIM), Inception Score (IS), Fréchet Inception Distance (FID), and Kernel Inception Distance (KID) assessed the model's prowess. Benchmarked against HR-VITON, ACGPN, and CP-VTON, our model slightly trailed HR-VITON but notably surpassed ACGPN and CP-VTON. In realistic, unpaired conditions, the model achieved an IS of 3.152, an FID of 15.3, and a KID of 0.0063. This is compared to an IS of 3.398, an FID of 11.93, and a KID of 0.0034 achieved by HR-VITON on the same data. ACGPN has an FID of 43.29, and a KID of 0.0373, while CP-VTON has an FID of 43.28, while it has a KID of 0.0376. IS is not measured for both ACGPN and CP-VTON. An ablation study underscored the importance of context-aware inpainting in our network. The findings highlight the model's ability to generate convincing, high-resolution virtual try-on images from garment-on-person extractions, addressing a prevalent gap in the literature and offering tangible applications in high-resolution virtual try-on image generation. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/40827 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:06.010Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/40827 High-resolution virtual try-on with garment extraction using generative adversarial networks Charters, Daniel J Britz, Stefan S Bernicchi, Dino data science Image-based virtual try-on aims to depict an individual wearing a garment not originally worn by them. While existing literature predominantly focuses on garments from standalone images, this research addresses the use of images where the garment is already being worn by another individual. The study bridges a notable gap as most current systems are tailored for standalone garment images. The proposed system, given a pair of high-resolution images, extracts the garment from one, refines it using context-aware image inpainting, and subsequently transfers it onto the second image's subject. The methodology incorporates various off-the-shelf models, notably Part Grouping Network (PGN), Densepose, and OpenPose for pre-processing. A state-of-the-art context-aware inpainting model refines the garments, and the final synthesis leverages the HR-VITON architecture, producing images at a resolution of 768 × 1024. Distinctively, our model processes both standalone and garment-on-person images. Evaluating the models involves testing on 2 032 high-resolution images under both paired and unpaired conditions. Metrics such as RMSE, Peak Signal-to-Noise Ratio (PSNR), Learned Perceptual Image Patch Similarity (LPIPS), Structural Similarity (SSIM), Inception Score (IS), Fréchet Inception Distance (FID), and Kernel Inception Distance (KID) assessed the model's prowess. Benchmarked against HR-VITON, ACGPN, and CP-VTON, our model slightly trailed HR-VITON but notably surpassed ACGPN and CP-VTON. In realistic, unpaired conditions, the model achieved an IS of 3.152, an FID of 15.3, and a KID of 0.0063. This is compared to an IS of 3.398, an FID of 11.93, and a KID of 0.0034 achieved by HR-VITON on the same data. ACGPN has an FID of 43.29, and a KID of 0.0373, while CP-VTON has an FID of 43.28, while it has a KID of 0.0376. IS is not measured for both ACGPN and CP-VTON. An ablation study underscored the importance of context-aware inpainting in our network. The findings highlight the model's ability to generate convincing, high-resolution virtual try-on images from garment-on-person extractions, addressing a prevalent gap in the literature and offering tangible applications in high-resolution virtual try-on image generation. 2025-01-23T09:17:42Z 2025-01-23T09:17:42Z 2024 2025-01-23T08:00:21Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/40827 eng application/pdf Department of Statistical Sciences Faculty of Science University of Cape Town |
| spellingShingle | data science Charters, Daniel J High-resolution virtual try-on with garment extraction using generative adversarial networks |
| thesis_degree_str | Master's |
| title | High-resolution virtual try-on with garment extraction using generative adversarial networks |
| title_full | High-resolution virtual try-on with garment extraction using generative adversarial networks |
| title_fullStr | High-resolution virtual try-on with garment extraction using generative adversarial networks |
| title_full_unstemmed | High-resolution virtual try-on with garment extraction using generative adversarial networks |
| title_short | High-resolution virtual try-on with garment extraction using generative adversarial networks |
| title_sort | high resolution virtual try on with garment extraction using generative adversarial networks |
| topic | data science |
| url | http://hdl.handle.net/11427/40827 |
| work_keys_str_mv | AT chartersdanielj highresolutionvirtualtryonwithgarmentextractionusinggenerativeadversarialnetworks |