Dress-up: deep neural framework for image-based human appearance transfer.

Hajer Ghodhbani, Mohamed Neji, Abdulrahman M Qahtani, Omar Almutiry, Habib Dhahri, Adel M Alimi

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Article in Multimedia tools and applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Hajer GhodhbaniREsearch Groups in Intelligent Machines (REGIM Lab), University of Sfax, National Engineering School of Sfax (ENIS), BP 1173, Sfax, 3038 Tunisia.ORCID 0000-0003-1100-0711

Mohamed NejiREsearch Groups in Intelligent Machines (REGIM Lab), University of Sfax, National Engineering School of Sfax (ENIS), BP 1173, Sfax, 3038 Tunisia.

Abdulrahman M QahtaniDepartment of Computer Science, College of Computers and Information Technology, Taif University, P.O.Box. 11099, Taif, 21944 Saudi Arabia.

Omar AlmutiryCollege of Applied Computer Science, King Saud University, Riyadh, Saudi Arabia.

Habib DhahriCollege of Applied Computer Science, King Saud University, Riyadh, Saudi Arabia.

Adel M AlimiREsearch Groups in Intelligent Machines (REGIM Lab), University of Sfax, National Engineering School of Sfax (ENIS), BP 1173, Sfax, 3038 Tunisia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The fashion industry is at the brink of radical transformation. The emergence of Artificial Intelligence (AI) in fashion applications creates many opportunities for this industry and make fashion a better space for everyone. Interesting to this matter, we proposed a virtual try-on interface to stimulate consumers purchase intentions and facilitate their online buying decision process. Thus, we present, in this paper, our flexible person generation system for virtual try-on that aiming to treat the task of human appearance transfer across images while preserving texture details and structural coherence of the generated outfit. This challenging task has drawn increasing attention and made huge development of intelligent fashion applications. However, it requires different challenges, especially in the case of a wide divergences between the source and target images. To solve this problem, we proposed a flexible person generation framework called

Indexed as

Artificial intelligenceGarment interchangeOutfit generationSemantic segmentationVirtual try-on

Identifiers

PMID36404934

DOI10.1007/s11042-022-14127-w

PMCPMC9652136

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.