Controllable Generative Systems for Fashion Prototyping: A Human–AI Collaborative Framework Based on Diffusion Models.
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| Title: | Controllable Generative Systems for Fashion Prototyping: A Human–AI Collaborative Framework Based on Diffusion Models. |
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| Authors: | Zhang, Guopeng1 (AUTHOR) zhangguopeng218@163.com |
| Source: | Computer Animation & Virtual Worlds. May/Jun2026, Vol. 37 Issue 3, p1-17. 17p. |
| Subjects: | Virtual prototypes, Human-artificial intelligence interaction, Probabilistic generative models, Generative artificial intelligence, Fashion design |
| Abstract: | Generative artificial intelligence is increasingly used in creative industries, yet its application in fashion design often remains limited to visual generation rather than structured design support. This study proposes a human‐in‐the‐loop controllable diffusion framework for virtual fashion prototyping. The pipeline integrates user‐refined garment masks, pose‐guided structural conditioning, sketch‐to‐render edge constraints, and lightweight LoRA style adaptation, enabling localized editing while preserving global body–garment coherence. Unlike prompt‐only image generation, conventional virtual try‐on systems, or downstream 3D production tools, the framework functions as a pre‐production visual ideation layer for designer‐led iterative editing. The study evaluates the framework through quantitative comparison with directly comparable GAN‐ and diffusion‐based baselines on DeepFashion2, using FID, LPIPS, SSIM, and Human Score; ablation analysis of individual control components; and a within‐subject user study design involving professional fashion designers across representative design tasks. A pilot evaluation with six experienced designers is also reported. Results show that the proposed framework outperforms directly comparable baselines across all reported metrics. Pilot findings further indicate higher perceived control, lower workload, and stronger usability than prompt‐only and inpainting baselines. The findings suggest that controllable diffusion can support professional fashion ideation as an interactive prototyping system rather than merely an automated image generator. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Animation & Virtual Worlds is the property of Wiley-Blackwell 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.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194920613 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Controllable Generative Systems for Fashion Prototyping: A Human–AI Collaborative Framework Based on Diffusion Models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Guopeng%22">Zhang, Guopeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhangguopeng218@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Animation+%26+Virtual+Worlds%22">Computer Animation & Virtual Worlds</searchLink>. May/Jun2026, Vol. 37 Issue 3, p1-17. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Virtual+prototypes%22">Virtual prototypes</searchLink><br /><searchLink fieldCode="DE" term="%22Human-artificial+intelligence+interaction%22">Human-artificial intelligence interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Probabilistic+generative+models%22">Probabilistic generative models</searchLink><br /><searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Fashion+design%22">Fashion design</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Generative artificial intelligence is increasingly used in creative industries, yet its application in fashion design often remains limited to visual generation rather than structured design support. This study proposes a human‐in‐the‐loop controllable diffusion framework for virtual fashion prototyping. The pipeline integrates user‐refined garment masks, pose‐guided structural conditioning, sketch‐to‐render edge constraints, and lightweight LoRA style adaptation, enabling localized editing while preserving global body–garment coherence. Unlike prompt‐only image generation, conventional virtual try‐on systems, or downstream 3D production tools, the framework functions as a pre‐production visual ideation layer for designer‐led iterative editing. The study evaluates the framework through quantitative comparison with directly comparable GAN‐ and diffusion‐based baselines on DeepFashion2, using FID, LPIPS, SSIM, and Human Score; ablation analysis of individual control components; and a within‐subject user study design involving professional fashion designers across representative design tasks. A pilot evaluation with six experienced designers is also reported. Results show that the proposed framework outperforms directly comparable baselines across all reported metrics. Pilot findings further indicate higher perceived control, lower workload, and stronger usability than prompt‐only and inpainting baselines. The findings suggest that controllable diffusion can support professional fashion ideation as an interactive prototyping system rather than merely an automated image generator. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Animation & Virtual Worlds is the property of Wiley-Blackwell 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: 10.1002/cav.70155 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Virtual prototypes Type: general – SubjectFull: Human-artificial intelligence interaction Type: general – SubjectFull: Probabilistic generative models Type: general – SubjectFull: Generative artificial intelligence Type: general – SubjectFull: Fashion design Type: general Titles: – TitleFull: Controllable Generative Systems for Fashion Prototyping: A Human–AI Collaborative Framework Based on Diffusion Models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Guopeng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May/Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 15464261 Numbering: – Type: volume Value: 37 – Type: issue Value: 3 Titles: – TitleFull: Computer Animation & Virtual Worlds Type: main |
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