{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T03:44:07Z","timestamp":1782186247484,"version":"3.54.5"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003789","name":"Helwan University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003789","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>In today\u2019s digital age, consumers increasingly rely on online shopping for convenience and accessibility. However, a significant drawback of online shopping is the inability to physically try on clothing before purchasing. This limitation often leads to uncertainty regarding fit and style, resulting in customer post-purchase dissatisfaction and higher return rates. Research indicates that online items are three times more likely to be returned than in-store ones, especially during the pandemic. To address this challenge, we propose a virtual try-on method called FITMI, an enhanced Latent Diffusion Textual Inversion model for virtual try-on purposes. The proposed architecture aims to bridge the gap between traditional in-store try-ons and online shopping by offering users a realistic and interactive virtual try-on experience. Although virtual try-on solutions already exist, recent advancements in artificial intelligence have significantly enhanced their capabilities, enabling more sophisticated and realistic virtual try-on experiences than ever before. Building on these advancements, FITMI surpasses ordinary virtual try-ons relying on generative adversarial networks, often producing unrealistic outputs. Instead, FITMI utilizes latent diffusion models to generate high-quality images with detailed textures. As a web application, FITMI facilitates virtual try-ons by seamlessly integrating images of users with garments from catalogs, providing a true-to-life representation of how the items would look. This approach differentiates us from competitors. FITMI is validated using two widely recognized benchmarks: the Dress-Code and Viton-HD datasets. Additionally, FITMI acts as a trusted style advisor, enhancing the shopping experience by recommending complementary items to elevate the chosen garment and suggesting similar options based on user preferences.<\/jats:p>","DOI":"10.1007\/s00521-024-10843-6","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T06:51:16Z","timestamp":1736405476000},"page":"6125-6144","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Revolutionizing online shopping with FITMI: a realistic virtual try-on solution"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5620-997X","authenticated-orcid":false,"given":"Tassneam M.","family":"Samy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beshoy I.","family":"Asham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salwa O.","family":"Slim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amr A.","family":"Abohany","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,9]]},"reference":[{"key":"10843_CR1","unstructured":"Ali H (2021) Real fashion dataset"},{"key":"10843_CR2","unstructured":"Bi\u0144kowski M, Sutherland DJ, Arbel M, Gretton A (2018) Demystifying mmd gans. arXiv preprint arXiv:1801.01401,"},{"key":"10843_CR3","doi-asserted-by":"crossref","unstructured":"Cao H, Tan C, Gao Z, Xu Y, Chen G, Heng P-A, Li SZ (2024) A survey on generative diffusion models. 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