Fashion Industry in the Age of Generative Artificial Intelligence and Metaverse: A systematic Review
- URL: http://arxiv.org/abs/2505.17141v1
- Date: Thu, 22 May 2025 07:06:27 GMT
- Title: Fashion Industry in the Age of Generative Artificial Intelligence and Metaverse: A systematic Review
- Authors: Rania Ahmed, Eman Ahmed, Ahmed Elbarbary, Ashraf Darwish, Aboul Ella Hassanien,
- Abstract summary: The fashion industry generates trillions of dollars in revenue by producing and distributing apparel, footwear, and accessories.<n>This systematic literature review ( SLR) seeks to systematically review and analyze the research landscape about the Generative Artificial Intelligence (GAI) and metaverse in the fashion industry.
- Score: 1.4796543791607084
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The fashion industry is an extremely profitable market that generates trillions of dollars in revenue by producing and distributing apparel, footwear, and accessories. This systematic literature review (SLR) seeks to systematically review and analyze the research landscape about the Generative Artificial Intelligence (GAI) and metaverse in the fashion industry. Thus, investigating the impact of integrating both technologies to enhance the fashion industry. This systematic review uses the Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology, including three essential phases: identification, evaluation, and reporting. In the identification phase, the target search problems are determined by selecting appropriate keywords and alternative synonyms. After that 578 documents from 2014 to the end of 2023 are retrieved. The evaluation phase applies three screening steps to assess papers and choose 118 eligible papers for full-text reading. Finally, the reporting phase thoroughly examines and synthesizes the 118 eligible papers to identify key themes associated with GAI and Metaverse in the fashion industry. Based on Strengths, Weaknesses, Opportunities, and Threats (SWOT) analyses performed for both GAI and metaverse for the fashion industry, it is concluded that the integration of GAI and the metaverse holds the capacity to profoundly revolutionize the fashion sector, presenting chances for improved manufacturing, design, sales, and client experiences. Accordingly, the research proposes a new framework to integrate GAI and metaverse to enhance the fashion industry. The framework presents different use cases to promote the fashion industry using the integration. Future research points for achieving a successful integration are demonstrated.
Related papers
- Composed Multi-modal Retrieval: A Survey of Approaches and Applications [81.54640206021757]
Composed Multi-modal Retrieval (CMR) emerges as a pivotal next-generation technology.<n>CMR enables users to query images or videos by integrating a reference visual input with textual modifications.<n>This paper provides a comprehensive survey of CMR, covering its fundamental challenges, technical advancements, and applications.
arXiv Detail & Related papers (2025-03-03T09:18:43Z) - Exploring the Technology Landscape through Topic Modeling, Expert Involvement, and Reinforcement Learning [0.48342038441006807]
This study proposes a method that combines topic modeling, expert knowledge inputs, and reinforcement learning (RL) to enhance the detection of technological changes.<n>Results demonstrate the method's effectiveness in identifying, ranking, and tracking trends that align with expert input.
arXiv Detail & Related papers (2025-01-22T22:18:50Z) - New Fashion Products Performance Forecasting: A Survey on Evolutions, Models and Emerging Trends [8.482907933021087]
The fast fashion industry's insatiable demand for new styles and rapid production cycles has led to a significant environmental burden.<n>To mitigate these issues, a paradigm shift that prioritizes sustainability and efficiency is urgently needed.<n>Integrating learning-based predictive analytics into the fashion industry represents a significant opportunity to address environmental challenges.
arXiv Detail & Related papers (2025-01-17T17:56:27Z) - IGGA: A Dataset of Industrial Guidelines and Policy Statements for Generative AIs [8.420666056013685]
This paper introduces IGGA, a dataset of 160 industry guidelines and policy statements for the use of Generative AIs (GAIs) and Large Language Models (LLMs) in industry and workplace settings.<n>The dataset contains 104,565 words and serves as a valuable resource for natural language processing tasks commonly applied in requirements engineering.
arXiv Detail & Related papers (2025-01-01T21:31:47Z) - Exploring the Future Metaverse: Research Models for User Experience, Business Readiness, and National Competitiveness [0.0]
The study examines the metaverse as a sociotechnical imaginary, enabled collectively by virtual reality (VR), augmented reality (AR), and mixed reality (MR) technologies.
We develop three research models, which can guide researchers in examining the metaverse as a sociotechnical future of information technology.
arXiv Detail & Related papers (2024-11-15T18:27:09Z) - From Linguistic Giants to Sensory Maestros: A Survey on Cross-Modal Reasoning with Large Language Models [56.9134620424985]
Cross-modal reasoning (CMR) is increasingly recognized as a crucial capability in the progression toward more sophisticated artificial intelligence systems.
The recent trend of deploying Large Language Models (LLMs) to tackle CMR tasks has marked a new mainstream of approaches for enhancing their effectiveness.
This survey offers a nuanced exposition of current methodologies applied in CMR using LLMs, classifying these into a detailed three-tiered taxonomy.
arXiv Detail & Related papers (2024-09-19T02:51:54Z) - Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML)
This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature.
The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
arXiv Detail & Related papers (2024-07-17T20:01:21Z) - FashionReGen: LLM-Empowered Fashion Report Generation [61.84580616045145]
We propose an intelligent Fashion Analyzing and Reporting system based on advanced Large Language Models (LLMs)
Specifically, it tries to deliver FashionReGen based on effective catwalk analysis, which is equipped with several key procedures.
It also inspires the explorations of more high-level tasks with industrial significance in other domains.
arXiv Detail & Related papers (2024-03-11T12:29:35Z) - Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models [52.24001776263608]
This comprehensive survey delves into the recent strides in HS moderation.
We highlight the burgeoning role of large language models (LLMs) and large multimodal models (LMMs)
We identify existing gaps in research, particularly in the context of underrepresented languages and cultures.
arXiv Detail & Related papers (2024-01-30T03:51:44Z) - Overview of the TREC 2023 Product Product Search Track [70.56592126043546]
This is the first year of the TREC Product search track.
The focus was the creation of a reusable collection.
We leverage the new product search corpus, which includes contextual metadata.
arXiv Detail & Related papers (2023-11-14T02:25:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.