“AI-POWERED FASHION SYSTEMS FOR PERSONALIZED OUTFIT RECOMMENDATIONS USING VIRTUAL TRY-ONS”

Abstract

ABSTRACT AI-POWERED FASHION SYSTEMS FOR PERSONALIZED OUTFIT RECOMMENDATIONS USING VIRTUAL TRY-ONS Fashion recommendation systems have undergone a remarkable evolution, transitioning from basic filtering methods to sophisticated AI-driven technologies that revolutionize the shopping experience. Early systems relied on static filters and basic collaborative filtering, which faced challenges like limited personalization and the cold start problem. The integration of hybrid models, machine learning, and computer vision in the 2010s marked a significant leap, enabling dynamic recommendations based on user preferences, visual attributes, and seasonal trends. Recent advancements include AR for immersive shopping, voice assistants for conversational recommendations, and real-time behavioral analysis for hyper-personalization. Emerging innovations, such as Wardrobe Wizard, exemplify this evolution by offering features like virtual outfit trials powered by diffusion models and algorithms for optimal outfit combinations. These systems enhance decision-making through realistic previews and tailored suggestions based on color theory and user preferences. Looking ahead, AI-powered ecosystems promise deeper personalization, integrating blockchain for transparency and smart devices for contextual recommendations, redefining the fashion landscape.

Specification

Description:Introduction
Fashion recommendation systems have evolved significantly since their inception, progressing through multiple technological innovations that have enhanced the shopping experience. This journey reflects advancements from rudimentary filtering options to the integration of cutting-edge technologies like AI, AR, and real-time behavioural analysis, shaping the way consumers discover and engage with fashion products.

In the early days of e-commerce, fashion recommendation systems were simplistic, offering basic filtering options based on product attributes such as size, colour, brand, and category. While these systems improved browsing efficiency, they were static and lacked the ability to learn from user preferences or behaviour. They functioned more as search tools rather than intelligent recommendation engines, leaving the customer to navigate vast product catalogues manually.

By the late 1990s and early 2000s, collaborative filtering algorithms emerged, leveraging user interaction data such as clicks, purchases, and ratings to recommend products. These systems operated on the principle that users with similar tastes would likely enjoy the same products. Although collaborative filtering improved recommendation quality, it faced challenges such as the cold start problem-where the system struggled to suggest products for new users or items with limited data.
To address these challenges, content-based filtering emerged in the early 2000s, focusing on product attributes (e.g., fabric type, pattern, or brand) to match items with users based on individual preferences. However, content-based systems alone were also limited-they could only recommend items similar to those the user had already shown interest in, restricting the diversity of recommendations.
Hybrid systems, combining both collaborative filtering and content-based models, emerged around the mid-2000s to overcome the limitations of each approach. These systems improved recommendation accuracy by blending multiple data sources, balancing user-based insights with product attributes. Retailers such as Amazon and eBay began implementing such hybrid solutions, marking a shift towards more personalized shopping experiences. This period also saw the advent of user reviews and ratings being integrated into recommendation systems, providing additional insights into consumer preferences.
In the 2010s, the rise of machine learning revolutionized fashion recommendation systems. Retailers began to analyse large datasets containing user interactions, purchase histories, and browsing behaviours to generate more dynamic and accurate recommendations.
Simultaneously, computer vision and image recognition technologies were introduced, enabling systems to analyse the visual attributes of clothing, such as colour, pattern, or texture. This allowed retailers to recommend visually similar items, helping users discover alternatives they might not have considered. For example, the system could suggest complementary pieces, such as matching shoes or accessories, based on the primary item a user was viewing.
Machine learning models were also trained to detect trends and seasonality, refining recommendations according to the latest fashion trends or seasonal needs (e.g., summer wear or festive outfits).

As mobile usage skyrocketed in the 2010s, recommendation systems became more tightly integrated into mobile shopping apps and social networks like Instagram and Pinterest. This allowed retailers to push personalized recommendations directly to users through in-app notifications and advertisements. The social media component also added a new dimension: social influence. Users could receive recommendations based on what their friends or influencers were wearing or buying, adding an aspirational element to product discovery.
Mobile and social integrations also facilitated real-time data collection, where recommendation engines adapted on-the-fly based on user activity, such as recent searches, clicks, or likes.

In the 2020s, retailers adopted AR technologies to enhance product discovery, allowing customers to visualize clothing in real-world settings. This boosted confidence in online purchases and reduced return rates, a major challenge in e-commerce.
At the same time, AI-powered voice assistants like Alexa and Google Assistant began offering personalized fashion recommendations through conversational interactions. These assistants suggested items based on purchase history, trends, and weather, simplifying the shopping experience and making it more relevant to users' needs.

Brief Description

AI-Driven Personalization and Real-Time Behavioural Analysis (Present and Future)
Fashion recommendation systems today are becoming more intelligent with the adoption of deep learning and natural language processing (NLP) models. These systems now understand nuanced preferences and make hyper-personalized recommendations based on factors like style preferences, body shape, and mood.
Moreover, behavioural analytics tools, such as Predictive Analytics Models (PAMs), are becoming integral. These models analyse a user's long-term behaviour and infer preferences, such as preferences for plain versus printed designs, colour palettes, or formals versus casuals. Through real-time behaviour tracking, recommendations are no longer limited to historical data-they dynamically evolve with each interaction, learning from user behaviour continuously.
Retailers are also beginning to cross-reference multiple data sources, such as weather patterns and social media trends, to tailor suggestions even further. For example, a recommendation system might promote raincoats or warm sweaters based on regional weather forecasts.

Looking ahead, AI-powered fashion ecosystems will likely dominate the landscape. These ecosystems will go beyond just recommending clothing items-they will integrate services such as:
• Subscription-based wardrobe services: AI will curate and deliver entire wardrobes based on user preferences and lifestyle needs.
• Virtual stylists: Personalized AI stylists will offer fashion advice, assisting users with outfit selection and wardrobe organization.
• Integration with smart devices: Future systems could connect with wearable technology, analysing health metrics (like body temperature) to suggest outfits suitable for a user's physical state.
Additionally, blockchain and decentralized fashion platforms might offer more transparent recommendations by verifying product authenticity and ensuring traceability.

Methodology
In today's fast-paced world, many individuals struggle with selecting suitable outfits for different occasions, often due to a lack of fashion sense or awareness of colour coordination. This can lead to wearing outfits that are either inappropriate for specific events or visually unappealing. To address this challenge, Wardrobe Wizard leverages advanced algorithms and AI-powered tools to provide users with personalized and practical fashion guidance.
Key Features: -
1. Best Combination

Users can store images of their clothes in a personalized digital wardrobe. The system applies matching algorithms to analyse these wardrobe items and suggest the best combinations based on colours, patterns, and user preferences. This feature ensures that users always have stylish and well-coordinated outfits readily available from their own collections.
2. Virtual Outfit Trial

This feature allows users to visualize how different outfits will look when worn. Users can upload their own photos or use an AI-generated fashion model to try on various outfits virtually. A diffusion model is employed to replace clothing in the uploaded images, generating realistic trial images that help users make informed fashion decisions.

Best Combinations
A recommendation system that processes user-uploaded clothing images to generate optimal outfit combinations. It stores the images in a database, isolates the clothing objects from their backgrounds, and determines the dominant colours for each item. These colours are mapped to their respective images in dictionaries, categorized by upper and lower clothing. A matching algorithm then evaluates all possible combinations of upper and lower garments against a predefined colour theory dataset to identify aesthetically pleasing matches. Finally, the system presents the valid combinations to the user or prompts them to try other options if no suitable matches are found.

Datasets
1) Colour Detection Dataset:
• Contains multiple user inputs of RGB values mapped to corresponding colour names.
• Training the model to recognize different colours.
2) Colour Combination Dataset:
• Contains pairs of complementary or harmonious colours based on colour theory.
• Enables the system to recommend pleasing outfit combinations.

Virtual Outfit Trial
The Virtual Outfit Trial system provides users with an immersive and personalized experience by allowing them to try on different clothing items virtually. Users can either upload their own photos or select from AI-generated fashion models to visualize how various outfits will appear when worn. At the core of this feature is a diffusion model, an advanced machine learning technique that plays a crucial role in replacing existing clothing in the image with the selected outfits.
The personally trained diffusion model is built on a diverse dataset that includes various clothing styles, body types, and poses. This focused training enables the model to accurately understand the nuances of fabric behaviour, ensuring that virtual outfits appear natural and flattering. By capturing details like folds and shadows, the model enhances the realism of clothing transformations, allowing users to experience a highly personalized and lifelike trial of different garments.
Working
The diffusion model works by gradually transforming the pixel distribution of the original image. It identifies the regions containing the existing clothing and replaces them with the new garments while maintaining the natural look of the image. During this process, the model preserves essential details such as the person's pose, skin texture, lighting, and shadows, ensuring a seamless blend between the original image and the new outfit. This transformation ensures that the final output looks realistic and free from distortions, providing the user with a clear and lifelike preview.
Output
Once the new clothing is applied, the system generates the output image, which is displayed to the user for review. This virtual try-on feature helps users explore various outfits and styling options without the need for physical trials, enhancing both convenience and decision-making. By leveraging cutting-edge AI technology, the Virtual Outfit Trial bridges the gap between physical and digital shopping experiences, offering a valuable tool for personalized fashion exploration.
Claims
To provide the recommendation system that helps the customers in selecting the best attire as per their choices using diffusion model.

To provide the virtual trial room to the customers so that they can refine their choices.
sers had their ratings on some items in the past will
have a similar rating on the other similar items in the future. Therefore, the similarity computed in
item space based on the content or features of these items. Thus, the relationship between the user's
items that the user has rated in the previous and other items in the database used to determine what
are the most suitable items for the target use
the idea that users had their ratings on some items in the past will
have a similar rating on the other similar items in the future. Therefore, the similarity computed in
item space based on the content or features of these items. Thus, the relationship between the user's
items that the user has rated in the previous and other items in the database used to determine what
are the most suitable items for the target use
the idea that users had their ratings on some items in the past will
have a similar rating on the other similar items in the future. Therefore, the similarity computed in
item space based on the content or features of these items. Thus, the relationship between the user's
items that the user has rated in the previous and other items in the database used to determine what
are the most suitable items for the target use
the idea that users had their ratings on some items in the past will
have a similar rating on the other similar items in the future. Therefore, the similarity computed in
item space based on the content or features of these items. Thus, the relationship between the user's
items that the user has rated in the previous and other items in the database used to determine what
are the most suitable items for the target use
have a similar rating on the other similar items in the future. Therefore, the similarity computed in
item space based on the content or features of these items. Thus, the relationship between the user's
items that the user has rated in the previous and other items in the database used to determine what
are the most suitable items for the target use
Literature Review
1) Fashion Recommendation Systems
Brief Description:
This research paper reviews the state-of-the-art fashion recommendation systems (FRSs) with a focus on filtering techniques used to personalize product recommendations for users in the fashion industry. It explores content-based filtering (CBF), collaborative filtering (CF), and hybrid models, discussing how these approaches leverage machine learning, computer vision, and user behaviour analysis to create personalized shopping experiences. The paper examines both the strengths and limitations of various filtering techniques and suggests future research areas, such as integrating social media images, using augmented reality (AR), and incorporating time series analysis to improve recommendation accuracy.
Pros:
1. Comprehensive Review: The paper provides an in-depth review of fashion recommendation systems, covering multiple filtering techniques and their applications.
2. Machine Learning Integration: It highlights the role of advanced machine learning algorithms (e.g., Bayesian classifier, neural networks) in enhancing recommendation accuracy.
3. Personalization: The research emphasizes personalized experiences for users, tailored to individual preferences and behaviours.
4. Future Opportunities: The paper discusses emerging areas like augmented and virtual reality in recommendation systems, offering valuable insight for future researchers.
5. Academic Contribution: It addresses a gap in the literature by being one of the first scholarly articles to systematically review fashion recommendation systems.
Cons:
1. Lack of Practical Implementation: The paper mainly discusses theoretical models and lacks substantial evidence from commercial applications, limiting its practical relevance.
2. Limited Focus on Real-time Systems: The paper does not explore the challenges of implementing real-time recommendation systems, particularly for fast fashion environments with constantly changing inventories.
3. Insufficient Exploration of Hybrid Models: While hybrid models are mentioned, the paper could delve deeper into the complexities and advantages of integrating content-based and collaborative filtering techniques.
4. Social Media Integration: Although it mentions the importance of social media, the paper lacks a detailed examination of how social media data can be effectively utilized in fashion recommendation systems.
5. Future Research Areas: The proposed future directions (like AR, time-series analysis) are promising but are only briefly touched upon without much elaboration on potential methodologies or challenges.

Citation [1]: Chakraborty, S., Hoque, Md. S., Jeem, N. R., Biswas, M. C., Bardhan, D., & Lobaton, E. (2021). Fashion Recommendation Systems, Models and Methods: a review. In Informatics (Vol. 8, p. 49). https://doi.org/10.3390/informatics8030049 (accessed 20 May 2024).

2) Image Processing for Colour Recognition
Brief Description:
This research focuses on colour detection in computer vision and uses machine learning to automate the process of identifying and categorizing colours within images. It explains the technical and conceptual challenges in teaching computers to understand and classify colours, using unsupervised learning methods like K-Means clustering to extract colours from images. The process includes image encoding, pixel analysis, and breaking down images into smaller sections for more accurate colour detection. The author concludes with reflections on the philosophical implications of machines being able to "see."
Pros:
1. No Large Dataset Requirement: The K-Means algorithm doesn't require training on vast datasets, making it accessible for various use cases.
2. Flexible Colour Clustering: The ability to choose the number of clusters (i.e., colours) provides flexibility in the level of colour detail captured.
3. Innovative Image Processing: The use of smaller image squares to analyse colour shows a creative approach to overcoming limitations in unsupervised learning.
4. Simplicity of Approach: The method described is relatively simple to implement using common libraries such as Sklearn, Numpy, and Pandas, making it accessible for those new to machine learning and computer vision.
5. Encourages Creativity: The paper encourages non-traditional methods in solving complex problems (e.g., negative pixel values), promoting practical solutions over rigid adherence to conventions.
Cons:
1. Limited Practical Applications Discussed: While the approach is innovative, the paper does not elaborate on how this colour detection technique could be applied to real-world problems or specific industries.
2. Inaccuracy in Colour Labelling: Since K-Means is unsupervised, it may not always accurately associate colours with predefined labels, leading to inconsistencies in results.
3. Over-Simplification: The process of colour detection is presented in a simplified manner, without addressing potential challenges in larger or more complex images.
4. Potential for Errors: The method's reliance on approximations and pixel averaging could lead to inaccuracies, especially in images with multiple similar colours or in cases where precise colour matching is required.
5. No Alternative Techniques Mentioned: The paper focuses solely on K-Means clustering without exploring other machine learning techniques or algorithms that could be used for similar tasks.
This paper serves as a solid introduction to colour detection using machine learning, though it could benefit from more discussion on practical applications and comparisons with other methods.

Citation [2]: Paialunga, P. (2023, January 3). Image colour identification with Machine Learning and Image Processing, using Python. Medium. https://towardsdatascience.com/image-colour-identification-with-machine-learning-and-image-processing-using-python-f3dd0606bdca (accessed 1 May 2024).

3) Colour Theory and Fashion Coordination
Brief Description:
This piece introduces basic concepts of colour theory, including the colour wheel, colour harmony, and colour context. It explains how primary, secondary, and tertiary colours are formed and explores how colours can be arranged to create harmony in visual designs. It also touches on how colour behaves in relation to other colours and shapes, emphasizing the relativity of colour perception. Examples include different colour schemes like analogous and complementary colours, as well as natural harmonies. The article provides practical formulas for creating colour harmony and addresses how contrasting backgrounds can alter the perception of the same colour.
Pros:
1. Comprehensive Overview: The piece provides a thorough introduction to fundamental colour theory concepts, making it accessible for readers new to the subject.
2. Clear Explanation of Colour Harmony: The text explains how different colour combinations can create visual harmony, presenting formulas for achieving balance, such as analogous and complementary colour schemes.
3. Use of Historical Context: The mention of Sir Isaac Newton's development of the colour wheel adds depth to the explanation and helps ground the discussion in historical context.
4. Practical Application Examples: The section on colour harmony in nature and complementary colour schemes provides real-world examples that make the concepts more relatable and easier to understand.
5. Explores Colour Perception: The analysis of how colours behave in relation to each other, especially under different backgrounds, offers valuable insights into colour relativity and perception.
Cons:
1. Limited Depth on Advanced Topics: While the article covers the basics well, it does not delve into more advanced aspects of colour theory, such as colour psychology or its use in branding and marketing.
2. Lack of Visual Aids: Despite discussing colour wheels, schemes, and examples of colour behaviour, the article would benefit from more visual illustrations to make these concepts easier to grasp.
3. Brief Exploration of Colour Context: The section on colour context is rather short, leaving out more detailed exploration of how colours interact in complex designs or in relation to cultural symbolism.
4. No Mention of Digital Colour Spaces: The article remains rooted in traditional colour theory, without addressing how digital environments, such as RGB and HEX colour models, differ from paint-based colour models.
5. Formulaic Nature: The article's focus on providing fixed formulas for colour harmony might oversimplify the creative process and limit experimentation, especially in artistic and non-traditional designs.
This article serves as a useful primer for those seeking to understand basic colour theory, but it could be enhanced with more visual examples and a deeper exploration of how colour behaves in modern digital environments.

Citation [3]: Basic Colour Theory. (n.d.-b). https://www.colourmatters.com/colour-and-design/basic-colour-theory (accessed 15 May 2024).

4) The State of Fashion
Brief Description:
This research paper from McKinsey, The State of Fashion 2024, offers a detailed analysis of the global fashion industry's performance, challenges, and emerging trends for 2024. It reflects on the resilience the industry has shown in previous years, especially the luxury segment, despite economic and geopolitical uncertainties. Looking forward, the paper identifies critical factors such as economic volatility, inflation, geopolitical tensions, and changing consumer preferences that will shape the fashion landscape. The report highlights key themes, including the growing influence of generative AI, sustainability regulations, shifts in consumer behaviour, and the competitive dynamics in fast fashion.
Pros:
1. Comprehensive Industry Analysis: The paper provides a detailed overview of the global fashion industry, focusing on key market segments like luxury and non-luxury, giving readers a clear understanding of the sector's dynamics.
2. Insightful Forecasting: The report forecasts future growth trends, particularly highlighting regional variations, which can help businesses prepare for potential challenges and opportunities.
3. Emerging Trends: The paper identifies critical themes such as generative AI, sustainability, and changing consumer behaviours (like outdoor lifestyle and travel preferences), offering fashion companies insights into how to adapt and innovate.
4. Global Perspective: By focusing on global markets, including China, Europe, the US, and India, the paper presents a holistic view of the fashion industry's trajectory across different regions.
5. Practical Recommendations: Through the exploration of themes like cost management, sustainability, and fast fashion competition, it provides practical strategies for businesses to navigate uncertainty.
Cons:
1. Heavy Focus on Luxury: While the luxury segment's performance is a major focus, the paper could have provided more depth on how smaller or mid-tier fashion brands are impacted by economic changes.
2. Uncertainty in Forecasting: Given the unpredictable nature of geopolitics and inflation, some forecasts may become quickly outdated, making the insights time sensitive.
3. Lack of In-Depth Focus on Consumer Shifts: While the paper discusses broad consumer shifts, it could have delved deeper into specific consumer behaviours, especially in emerging markets.
4. Generalized Recommendations: Some recommendations, particularly around sustainability and cost-saving tactics, may appear generalized without specific action plans tailored for companies of different sizes or market focuses.
5. Overemphasis on AI: While generative AI is a crucial trend, the paper might place too much emphasis on its impact, potentially overlooking other areas of innovation that could also shape the industry.

Citation [4]: Balchandani, A., Barrelet, D., Berg, A., D'Auria, G., Rölkens, F., & Starzynska, E. (2023). The State of Fashion 2024: Finding pockets of growth as uncertainty reigns. In McKinsey & Company. https://www.mckinsey.com/industries/retail/our-insights/state-of-fashion (accessed 20 June 2024).

5) Fashion Revolution Survey
Brief Description:
The Fashion Revolution Consumer Survey 2020 is a follow-up to the initial 2018 survey, commissioned to understand how sustainability and supply chain transparency influence the purchasing decisions of European consumers. Conducted in the five largest European markets-Germany, France, Italy, Spain, and the UK-the survey targeted people aged 16-75. It explored consumers' awareness of fashion's environmental and social impacts and their expectations from fashion brands and governments regarding transparency, ethical practices, and sustainability. The survey aimed to shed light on what information consumers desire from brands and the role they believe governments should play in ensuring sustainable fashion production. The findings highlight consumer demand for more transparency and ethical production processes within the fashion industry and suggest opportunities for brands, governments, and consumers to drive industry-wide changes.
________________________________________
Pros:
1. Increased Awareness: The survey reveals a growing awareness and demand from consumers for fashion brands to act ethically, with a notable rise in those wanting to know how their clothes are made and advocating for improved conditions for garment workers.
2. Clear Action Points for Brands: The findings provide fashion brands with actionable insights on what consumers expect-such as more transparency in supply chains, ethical certifications, and sustainability practices.
3. Focus on Government Role: Consumers believe governments should play a more active role in ensuring sustainable fashion, encouraging policy makers to consider stricter regulations for the fashion industry.
4. Long-Term Wear: Many consumers indicate they wear clothes for several years and pass them on for reuse, demonstrating a shift towards more sustainable behaviour and reduced consumption.
5. Cross-Country Comparisons: The survey provides interesting insights into how different countries approach clothing consumption, offering targeted insights for businesses operating across these regions.
________________________________________
Cons:
1. Limited Behavioural Change: Despite increased awareness, the survey highlights that only a minority of consumers actively avoid buying new clothes or repair damaged items, indicating a gap between awareness and action.
2. Price Over Sustainability: Many consumers still prioritize purchasing clothing on sale over buying items made responsibly, reflecting that cost remains a key factor, which may hinder the growth of sustainable fashion.
3. Low Second-Hand Purchases: The percentage of consumers purchasing second-hand clothing is relatively low, with only 14% making this choice, suggesting the slow adoption of circular fashion practices.
4. Insufficient Attention to Environmental Impact: While consumers express a desire to buy clothing that does not harm the environment, less than half wash their clothes at lower temperatures to reduce their carbon footprint, revealing inconsistencies in their environmental consciousness.
5. Generational Gaps in Consumption Patterns: Younger consumers (aged 16-24) tend to prioritize fashion trends over sustainability, with 8% saying they only wear "in-fashion" clothes, pointing to challenges in convincing younger generations to adopt sustainable practices.

Citation [5]: Consumer Survey Report: Fashion Revolution. (n.d.). https://www.fashionrevolution.org/resources/consumer-survey (accessed 2 July 2024).

6) Artificial Intelligence in Fashion
Brief Description:
The research paper "The Role of Generative AI in the Fashion Industry" explores how artificial intelligence (AI) is revolutionizing various aspects of the fashion sector. It highlights AI's potential to significantly impact the industry by predicting trends, optimizing supply chains, personalizing shopping experiences, and combating counterfeit products. The paper provides examples of how AI technologies, such as trend prediction algorithms, supply chain optimizers, virtual try-on solutions, and personalized recommendation systems, are being employed by major fashion brands and tech companies. It also discusses the ethical considerations of AI use, including data privacy and algorithmic bias, and concludes by emphasizing the transformative potential of AI in driving efficiency, sustainability, and innovation in the fashion industry.
Pros:
1. Comprehensive Coverage: The paper thoroughly examines multiple facets of AI's impact on the fashion industry, including trend prediction, supply chain optimization, personalization, and anti-counterfeiting measures.
2. Real-World Examples: It provides concrete examples of how AI is being implemented by major brands and tech companies, which helps in understanding practical applications and benefits.
3. Future Potential: The paper highlights the significant potential financial impact of AI on the fashion industry, demonstrating its role as a key driver of growth and innovation.
4. Sustainability Focus: The research underscores AI's role in promoting sustainability by reducing waste and improving supply chain efficiency.
5. Ethical Considerations: It addresses important ethical issues related to data privacy and algorithmic bias, advocating for transparency and accountability.
Cons:
1. Lack of Depth in Ethical Issues: While ethical considerations are mentioned, the discussion may not be deep enough to address all potential concerns and solutions comprehensively.
2. Dependency on Technology: The paper may underemphasize the risks of over-reliance on AI, such as potential job losses or the digital divide.
3. Limited Scope of Examples: The examples provided are primarily from well-known companies, which may not represent the challenges and benefits experienced by smaller or less tech-savvy fashion brands.
4. Generalization of AI Benefits: The paper might generalize AI's benefits without fully exploring the complexities and limitations of its implementation in diverse fashion contexts.
5. Potential Bias: The research could exhibit a positive bias towards AI technologies without equally weighing possible drawbacks or unintended consequences.

Citation [6]: Christou, L. (2024, July 15). Artificial Intelligence in Fashion: Reshaping the Entire Industry. 3DLOOK. https://3dlook.ai/content-hub/artificial-intelligence-in-fashion/ (accessed 7 July, 2024).

Results

Best Combinations

Virtual Outfit Trial

Dataset for Upper Clothes

Dataset for Lower Clothes

Purchase Recommendations

, Claims:To provide the recommendation system that helps the customers in selecting the best attire as per their choices using diffusion model.

To provide the virtual trial room to the customers so that they can refine their choices.

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