Academic Journal
Computer Vision for Fashion: A Systematic Review of Design Generation, Simulation, and Personalized Recommendations.
| Τίτλος: | Computer Vision for Fashion: A Systematic Review of Design Generation, Simulation, and Personalized Recommendations. |
|---|---|
| Συγγραφείς: | Kachbal, Ilham, El Abdellaoui, Said |
| Πηγή: | Information; Jan2026, Vol. 17 Issue 1, p11, 49p |
| Θεματικοί όροι: | Computer vision, Artificial intelligence, Digital computer simulation, Virtual design, Clothing industry, Recommender systems, Sustainability, Deep learning |
| Περίληψη: | The convergence of fashion and technology has created new opportunities for creativity, convenience, and sustainability through the integration of computer vision and artificial intelligence. This systematic review, following PRISMA guidelines, examines 200 studies published between 2017 and 2025 to analyze computational techniques for garment design, accessories, cosmetics, and outfit coordination across three key areas: generative design approaches, virtual simulation methods, and personalized recommendation systems. We comprehensively evaluate deep learning architectures, datasets, and performance metrics employed for fashion item synthesis, virtual try-on, cloth simulation, and outfit recommendation. Key findings reveal significant advances in Generative adversarial network (GAN)-based and diffusion-based fashion generation, physics-based simulations achieving real-time performance on mobile and virtual reality (VR) devices, and context-aware recommendation systems integrating multimodal data sources. However, persistent challenges remain, including data scarcity, computational constraints, privacy concerns, and algorithmic bias. We propose actionable directions for responsible AI development in fashion and textile applications, emphasizing the need for inclusive datasets, transparent algorithms, and sustainable computational practices. This review provides researchers and industry practitioners with a comprehensive synthesis of current capabilities, limitations, and future opportunities at the intersection of computer vision and fashion design. [ABSTRACT FROM AUTHOR] |
| Copyright of Information is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Computer Vision for Fashion: A Systematic Review of Design Generation, Simulation, and Personalized Recommendations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kachbal%2C+Ilham%22">Kachbal, Ilham</searchLink><br /><searchLink fieldCode="AR" term="%22El+Abdellaoui%2C+Said%22">El Abdellaoui, Said</searchLink> – Name: TitleSource Label: Source Group: Src Data: Information; Jan2026, Vol. 17 Issue 1, p11, 49p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+computer+simulation%22">Digital computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Virtual+design%22">Virtual design</searchLink><br /><searchLink fieldCode="DE" term="%22Clothing+industry%22">Clothing industry</searchLink><br /><searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink><br /><searchLink fieldCode="DE" term="%22Sustainability%22">Sustainability</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The convergence of fashion and technology has created new opportunities for creativity, convenience, and sustainability through the integration of computer vision and artificial intelligence. This systematic review, following PRISMA guidelines, examines 200 studies published between 2017 and 2025 to analyze computational techniques for garment design, accessories, cosmetics, and outfit coordination across three key areas: generative design approaches, virtual simulation methods, and personalized recommendation systems. We comprehensively evaluate deep learning architectures, datasets, and performance metrics employed for fashion item synthesis, virtual try-on, cloth simulation, and outfit recommendation. Key findings reveal significant advances in Generative adversarial network (GAN)-based and diffusion-based fashion generation, physics-based simulations achieving real-time performance on mobile and virtual reality (VR) devices, and context-aware recommendation systems integrating multimodal data sources. However, persistent challenges remain, including data scarcity, computational constraints, privacy concerns, and algorithmic bias. We propose actionable directions for responsible AI development in fashion and textile applications, emphasizing the need for inclusive datasets, transparent algorithms, and sustainable computational practices. This review provides researchers and industry practitioners with a comprehensive synthesis of current capabilities, limitations, and future opportunities at the intersection of computer vision and fashion design. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Information is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/info17010011 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 49 StartPage: 11 Subjects: – SubjectFull: Computer vision Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Digital computer simulation Type: general – SubjectFull: Virtual design Type: general – SubjectFull: Clothing industry Type: general – SubjectFull: Recommender systems Type: general – SubjectFull: Sustainability Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Computer Vision for Fashion: A Systematic Review of Design Generation, Simulation, and Personalized Recommendations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kachbal, Ilham – PersonEntity: Name: NameFull: El Abdellaoui, Said IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20782489 Numbering: – Type: volume Value: 17 – Type: issue Value: 1 Titles: – TitleFull: Information Type: main |
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