Fashion Outfit Research Articles

StyleSphere: Conversational Fashion Outfit Generator powered by Generative AI

Artificial Intelligence (AI) has gained traction in the fashion domain, particularly with Generative AI technologies. Our study presents a unique text-to-image generator tailored for fashion, leveraging Generative Adversarial Networks (GANs), notably the StackGAN variant. This model translates textual fashion descriptions into visually appealing images, offering a stream-lined approach to design conception. Validation demonstrates its ability to produce photo-realistic fashion visuals, bridging the gap between concept and visualization. Our research con-tributes to advancing AI-driven solutions in fashion, facilitating streamlined design workflows and encouraging creativity. This integration of AI with fashion design practices has the potential to revolutionize the industry, offering new avenues for creative expression and addressing contemporary design challenges. Keywords Fashion Design, Generative Adversarial Networks (GANs), StackGAN, Image Synthesis

Conversational Fashion Outfit Generator Powered by GenAI

The convergence of artificial intelligence and fashion has given rise to innovative solutions that cater to the ever-evolving needs and preferences of fashion enthusiasts. This report delves into the methodology behind the development of a "Conversational Fashion Outfit Generator powered by GenAI," an advanced application that leverages the capabilities of Generative Artificial Intelligence (GenAI) to create personalized fashion outfits through natural language interactions. The model outlines the essential elements of the methodology, including data collection, natural language understanding, computer vision integration, and deep learning algorithms. Data collection forms the bedrock, as access to a diverse dataset of fashion-related information is critical for training and fine-tuning AI models. Natural Language Understanding (NLU) is instrumental in comprehending user input and generating context-aware responses, ensuring meaningful and engaging conversations. Computer vision technology is integrated to analyze fashion images, recognizing clothing items, styles, and colors, thus aiding in outfit recommendations. Deep learning algorithms, particularly recurrent and transformer-based models, form the backbone of the system, generating personalized and contextually relevant fashion suggestions. This methodology not only underpins the "Conversational Fashion Outfit Generator" but also reflects the evolving landscape of AI in the fashion industry, where personalized, interactive experiences are becoming increasingly paramount in the realm of fashion and e-commerce.

Learning to Synthesize Compatible Fashion Items Using Semantic Alignment and Collocation Classification: An Outfit Generation Framework.

The field of fashion compatibility learning has attracted great attention from both the academic and industrial communities in recent years. Many studies have been carried out for fashion compatibility prediction, collocated outfit recommendation, artificial intelligence (AI)-enabled compatible fashion design, and related topics. In particular, AI-enabled compatible fashion design can be used to synthesize compatible fashion items or outfits to improve the design experience for designers or the efficacy of recommendations for customers. However, previous generative models for collocated fashion synthesis have generally focused on the image-to-image translation between fashion items of upper and lower clothing. In this article, we propose a novel outfit generation framework, i.e., OutfitGAN, with the aim of synthesizing a set of complementary items to compose an entire outfit, given one extant fashion item and reference masks of target synthesized items. OutfitGAN includes a semantic alignment module (SAM), which is responsible for characterizing the mapping correspondence between the existing fashion items and the synthesized ones, to improve the quality of the synthesized images, and a collocation classification module (CCM), which is used to improve the compatibility of a synthesized outfit. To evaluate the performance of our proposed models, we built a large-scale dataset consisting of 20 000 fashion outfits. Extensive experimental results on this dataset show that our OutfitGAN can synthesize photo-realistic outfits and outperform the state-of-the-art methods in terms of similarity, authenticity, and compatibility measurements.

Set representative vector and its asymmetric attention-based transformation for heterogeneous set-to-set matching

Heterogeneous set-to-set matching, applied to fashion outfit recommendations, no longer depends on the similarity but on compatibility between items in sets. Existing state-of-the-art methods apply self- and cross-attention mechanisms to transform item vectors closer or farther apart and compute the matching score based on transformed item vectors. However, the transformation by attention mechanisms with fewer items in sets is performed in rather limited spaces, and the complex computation of scores in the head network would cause the instability of the training of entire networks. To overcome these problems, we propose to add a trainable set representative vector into each set and to embed discriminative information of items onto the vector through originally extended asymmetric attention mechanisms. This enables dynamic transformation in wider spaces and stable training without gradient vanishing problems to generate discriminative matching scores. Through experiments with heterogeneous set-to-set matching tasks, even-total, and fashion outfit matching tasks, we show the effectiveness of our proposed method.

Mzansi Magic

Mzansi Magic

The Effect of Advertising Disclosure Language and Celebrity Endorsement on Tiktok on Purchasing Decisions for Fashion Outfit Products

This study aims to determine the effect of knowledge and capability on employee performance at PT. Ramajaya Pramukti Tapung, Kampar Regency. The population in this study were all employees at PT. Ramajaya Pramukti Tapung, Kampar Regency. The sample of this study used the technique of the census method so that the sample was all employees at PT. Ramajaya Pramukti Tapung Kampar Regency as many as 100 people. Data collection techniques used are questionnaires, observations and research files. The data analysis technique used is multiple linear regression analysis with a significant level (α) of 0.05. The results of this study conclude that Knowledge and Capability have a partially significant effect on employee performance, with a significant value less than 0.05. The results of the analysis of this study indicate that Knowledge and Capability have a significant simultaneous effect on employee performance at PT. Ramajaya Pramukti Tapung Kampar Regency. There is a very strong relationship between the independent variable and the dependent variable. The contribution of Knowledge and Capability variables to employee performance is 85.80%, which is very high.

The Effect of Advertising Disclosure Language and Celebrity Endorsement on Tiktok on Purchasing Decisions for Fashion Outfit Products

Today's growing promotional media greatly affects the digital world and product marketing on purchasing decisions. The Effect of Advertising Disclosure Language and Celebrity Endorsment on Purchasing Decisions for Fashion Outfit products is research within the scope of management science. The aim is to analyze and examine the effect of advertising disclosure language and celebrity endorsment on tiktok on purchasing decisions for fashion outfit products for teenagers, especially students at dharmas indonesia university. The method used is quantitative method. data collection using a questionnaire. Data analysis techniques with Validity test, Instrument reliability test, Correlation analysis, Hypothesis test, Multiple Regression analysis, Classical Assumption test, Multiple linear Regression analysis. The population in this study were 1,811 respondents and the samples in this study were 292 who were students of Dharmas Indonesia University. The results of this study: 1) Advertising Disclosure Language affects the purchase decision of Fashion Outfit products; and 2) Celebrity Endorsment has an effect on purchasing decisions for Fashion Outfit products.

Partial visual-semantic embedding: Fine-grained outfit image representation with massive volumes of tags via angular-based contrastive learning

A novel technology named fashion intelligence system, which quantifies ambiguous expressions unique to fashion, such as “casual,” “adult-casual,” and “office-casual,” was previously proposed to support users in their understanding of fashion. However, the existing visual-semantic embedding (VSE) model, which forms the basis of the system, does not support images that are composed of multiple parts, such as those containing hair, tops, trousers, skirts, and shoes. Therefore, we propose a partial VSE (PVSE) model, which enables fine-grained learning of each part of the fashion outfit. The proposed model learns embedded representations via angular-based contrastive learning. This helps in retaining three existing practical functionalities and further enables image-retrieval tasks where changes are only made to specified parts and image-reordering tasks focusing on the specified parts. In other words, the proposed model enables five types of practical functionalities, even with a simple structure. Through qualitative and quantitative experiments, we demonstrate that the proposed model is superior to conventional models, without increasing computational complexity.

Personalized fashion outfit generation with user coordination preference learning

This paper focuses on personalized outfit generation, aiming to generate compatible fashion outfits catering to given users. Personalized recommendation by generating outfits of compatible items is an emerging task in the recommendation community with great commercial value but less explored. The task requires to explore both user-outfit personalization and outfit compatibility, any of which is challenging due to the huge learning space resulted from large number of items, users, and possible outfit options. To specify the user preference on outfits and regulate the outfit compatibility modeling, we propose to incorporate coordination knowledge in fashion. Inspired by the fact that users might have coordination preference in terms of category combination, we first define category combinations as templates and propose to model user-template relationship to capture users’ coordination preferences. Moreover, since a small number of templates can cover the majority of fashion outfits, leveraging templates is also promising to guide the outfit generation process. In this paper, we propose Template-guided Outfit Generation (TOG) framework, which unifies the learning of user-template interaction, user–item interaction and outfit compatibility modeling. The personal preference modeling and outfit generation are organically blended together in our problem formulation, and therefore can be achieved simultaneously. Furthermore, we propose new evaluation protocols to evaluate different models from both the personalization and compatibility perspectives. Extensive experiments on two public datasets have demonstrated that the proposed TOG can achieve preferable performance in both evaluation perspectives, namely outperforming the most competitive baseline BGN by 7.8% and 10.3% in terms of personalization precision on iFashion and Polyvore datasets, respectively, and improving the compatibility of the generated outfits by over 2%.

CLOTHING FASHION IMAGE GENERATION FROM TEXT USING ARTIFICIAL INTELLIGENCE

Development of dynamic, intensely engaging, and fascinating images has greatly benefited from the recent exponential advancements in image synthesis techniques. The architecture proposed in this research allows users to enter text regarding a particular dress, and the model then create images of fashionable apparel based on that content. The model suggested can let people become their own fashion designers by utilizing the strength of Deep Learning and Artificial intelligence to create a variety of fashionable outfits for themselves. DALL-E model is utilized to engender realistic images based on text description. DALL-E is an artificial intelligence model that generates realistic images from a description in natural language. While there are alternative text-to-image systems, the DALL-E produces far more coherent visuals. The world and the relationships between objects appear to be well understood by this technology. DALL-E uses GPT-3 model and dataset of textimage pairs for image synthesis. Image is encoded into size of 32×32 grid using VQ-VAE. Then image and text are combined together in the form of single stream for training of DALL-E. Deep Fashion dataset is used for training of DALL-E, which is simply more realistic dataset and contains High definition images that further enable accurate generation. After training DALL-E produce more accurate results and provides higher inception score than preceding models.

Fashion Outfit Recommendation and Transformation Using Generative Adversarial Network Methods

Over the last few decades, generative adversarial network (GAN) methods have been increasingly applied and improved in fashion-related scenes, especially during the pandemic. This article provides an overview of several mainstream GAN models for fashion recommendation and fashion transformation, including compatibility matrix-regularized GAN, Outfit GAN, transformer-based GAN, conditional analogy GAN, disentangled cycle-consistency try-on network, and flow-navigated warping GAN. Within each GAN method, the improvements made to perform more related and deliciated are presented with detailed loss functions, the specific evaluation matrixes, and the examinations of generators or discriminators. Moreover, possible directions of improvements for future research and problems with existing models are involved. As for the common restrictions of included methods, including the problem of incomprehensive and non-diverse datasets, the standard evaluation of modish aesthetic value, and the number of fashion items involved in one outfit, are analyzed within fashion outfit recommendation and fashion transformation. Therefore, such restrictions provide possible directions for future analysis in fashion-related networks.

The Effect of Social Appearance Anxiety on the Online Impulse Purchases of Fashionable Outfits Among Female College Students During Pandemic Periods: The Mediating Role of Self-Control and the Moderating Role of Subjective Socioeconomic Status.

With the rise of social media, an increasing number of young females have focused more attention on their body image, leading to social appearance anxiety. Thus, the current study aimed to investigate a moderated mediation model of social appearance anxiety and online impulse purchases of fashionable outfits through self-control moderated by subjective socioeconomic status during the pandemic. A total of 1651 female college students (Age = 17-24, Mage = 19.30, SD = 1.14) with more than one month of closed-off management experience completed self-report questionnaires concerning social appearance anxiety, self-control, online impulse purchases of fashionable outfits, and subjective socioeconomic status. After controlling for the potential influence of coronavirus stress, the results indicated that social appearance anxiety was positively related to online impulse purchases of fashionable outfits among female college students during the pandemic, mediated by self-control. Furthermore, subjective socioeconomic status moderated the indirect link between social appearance anxiety and the online impulse purchase of fashionable outfits. Specifically, subjective socioeconomic status buffers the negative effect of social appearance anxiety on self-control and the risk effect of low self-control on the online impulse purchases of fashionable outfits. The current study deepens the research on the relationship between social appearance anxiety and online impulse purchases during pandemic periods; meanwhile, it provides evidence for preventing excessive online impulse purchases of fashionable outfits among young females.

VICTOR: Visual incompatibility detection with transformers and fashion-specific contrastive pre-training

For fashion outfits to be considered aesthetically pleasing, the garments that constitute them need to be compatible in terms of visual aspects, such as style, category and color. Previous works have defined visual compatibility as a binary classification task with items in a garment being considered as fully compatible or fully incompatible. However, this is not applicable to Outfit Maker applications where users create their own outfits and need to know which specific items may be incompatible with the rest of the outfit. To address this, we propose the Visual InCompatibility TransfORmer (VICTOR) that is optimized for two tasks: 1) overall compatibility as regression and 2) the detection of mismatching items and utilize fashion-specific contrastive language-image pre-training for fine tuning computer vision neural networks on fashion imagery. We build upon the Polyvore outfit benchmark to generate partially mismatching outfits, creating a new dataset termed Polyvore-MISFITs, that is used to train VICTOR. A series of ablation and comparative analyses show that the proposed architecture can compete and even surpass the current state-of-the-art on Polyvore datasets while reducing the instance-wise floating operations by 88%, striking a balance between high performance and efficiency. We release our code at https://github.com/stevejpapad/Visual-InCompatibility-Transformer

COutfitGAN: Learning to Synthesize Compatible Outfits Supervised by Silhouette Masks and Fashion Styles

How to recommend outfits has gained considerable attention in both academia and industry in recent years. Many studies have been carried out regarding fashion compatibility learning, to determine whether the fashion items in an outfit are compatible or not. These methods mainly focus on evaluating the compatibility of existing outfits and rarely consider applying such knowledge to ‘design’ new fashion items. We propose the new task of generating complementary and compatible fashion items based on an arbitrary number of given fashion items. In particular, given some fashion items that can make up an outfit, the aim of this paper is to synthesize photo-realistic images of other, complementary, fashion items that are compatible with the given ones. To achieve this, we propose an outfit generation framework, referred to as COutfitGAN, which includes a pyramid style extractor, an outfit generator, a UNet-based real/fake discriminator, and a collocation discriminator. To train and evaluate this framework, we collected a large-scale fashion outfit dataset with over 200K outfits and 800K fashion items from the Internet. Extensive experiments show that COutfitGAN outperforms other baselines in terms of similarity, authenticity, and compatibility measurements.

Fill in the blank for fashion complementary outfit product Retrieval: VISUM summer school competition

Every year, the VISion Understanding and Machine intelligence (VISUM) summer school runs a competition where participants can learn and share knowledge about Computer Vision and Machine Learning in a vibrant environment. 2021 VISUM’s focused on applying those methodologies in fashion. Recently, there has been an increase of interest within the scientific community in applying computer vision methodologies to the fashion domain. That is highly motivated by fashion being one of the world’s largest industries presenting a rapid development in e-commerce mainly since the COVID-19 pandemic. Computer Vision for Fashion enables a wide range of innovations, from personalized recommendations to outfit matching. The competition enabled students to apply the knowledge acquired in the summer school to a real-world problem. The ambition was to foster research and development in fashion outfit complementary product retrieval by leveraging vast visual and textual data with domain knowledge. For this, a new fashion outfit dataset (acquired and curated by FARFETCH) for research and benchmark purposes is introduced. Additionally, a competitive baseline with an original negative sampling process for triplet mining was implemented and served as a starting point for participants. The top 3 performing methods are described in this paper since they constitute the reference state-of-the-art for this particular problem. To our knowledge, this is the first challenge in fashion outfit complementary product retrieval. Moreover, this joint project between academia and industry brings several relevant contributions to disseminating science and technology, promoting economic and social development, and helping to connect early-career researchers to real-world industry challenges.

Diagnosing fashion outfit compatibility with deep learning techniques

Fashion image understanding is a popular research field with many different machine learning applications. There have been many studies regarding outfit prediction and outfit composition in the field of fashion. However, there are few works that explain the prediction. This paper addresses a method of diagnosing outfit compatibility through clothing images. The proposed system not only predicts compatibility, but also diagnoses incompatible clothing items in outfits. First, a new dataset named ModAI, which has clothing images and compatibility comments from different users was created. After this, a common compatibility comment was created according to user comments for each clothing image. Lastly, image captioning techniques were used to generate compatibility suggestion texts from clothing images. Different segmentation techniques were also used to improve captioning capabilities. The model achieves a 0.62 BLEU-4 score. Experiments show that image captioning techniques can also be used to diagnose outfit compatibility.

Kawaii Affective Assemblages

Kawaii Affective Assemblages

A fashionable wedding in Dakar, Senegal

A young bride and groom, Zeynab Dieng (25) and Abdoulaye Ly (28), asked me to photograph their Muslim/Peulh wedding festivities in Dakar, Senegal. Dakarois wedding celebrations centre around the bride, who demonstrates her dignity and savoir faire as a new matron by appearing in a succession of fashionable outfits. Within the Senegalese fashion system, these outfits fall into the style categories labelled ‘ethnic’, ‘pan-African’ and ‘European’. Among the many Senegalese weddings that I have attended and photographed, the wedding of Abdoulaye and Zeynab was startlingly original in its fashions and its rituals. Abdoulaye, Zeynab and their siblings performed a ceremony of freedom on their own terms. My study addresses interwoven processes of colonization/decolonization. The first concerns my own far-from-complete process of attempting to decolonize my mind and practice. The second process concerns the refusing-to-die colonial dichotomy of ‘tradition’ and ‘modernity’ with respect to fashion and weddings. Instead of applying colonial labels to the fashions and the wedding, I try to see these through the lens of the Dakarois youth imaginary. How do Zeynab, Abdoulaye and their contemporaries interpret the decolonizing/neo-colonizing forces they navigate in creating fashions and in shaping their own lives?

Creativity Ratings of Fashion Outfits Presented on Instagram: Does Gender Matter?

Abstract Rationale: Creativity assessment can be influenced by rater characteristics, including social group membership, such as gender. As raters are often male, the gender composition of rater panels in the Consensual Assessment Technique (CAT) could introduce unintended implicit biases into this measurement methodology. The present study analyzed such biases by examining gender differences in creativity assessment. Method: We applied the CAT and asked male (n = 26) and female (n = 39) judges to rate the creativity of fashion outfits presented on Instagram. We then examined gender differences in mean creativity ratings and rater consistency (inter-rater reliability). In an additional qualitative analysis, we analyzed implicit theories of creativity of female and male raters by comparing the criteria that these raters applied when assessing creativity. Results: We found no systematic support for gender differences in the level of creativity ratings, but observed that rating consistency was significantly higher for female than for male judges. Additional content analysis suggested that female and male raters attached different relative importance to various assessment criteria, indicating gender differences in rating criteria. Discussion: Our study suggests that rater panel composition can indeed affect aspects of creativity assessment, although we do not obtain strong support for a gender-related bias in the CAT methodology.

Learning compatibility knowledge for outfit recommendation with complementary clothing matching

With the rapid development of mobile networks and e-commerce, clothing recommendation has achieved considerable success in recent years. Fashion outfit matching has become an essential component to users while shopping, which helps users to select and present items to individuals in a personalized fashion recommendation. Apparently, it is an arduous task to guide complementary clothing matching due to the complexity and subjectivity of fashion items. Some existing solutions have been presented in recent years, which are tending to discover a series of visual cues to establish the matching relations. However, it would be mismatched easily due to these methods being hard to represent all the potential semantic information from the appearance of clothes. To thoroughly make use of the visual characteristics of clothing products and the related description information, we propose a complementary clothing matching method with some compatibility knowledge, named it CCMCK shortly. For visual compatibility, we adopt the graph neural network to model the visual relationship between items. To generate an outfit that satisfies the requirement of fashion compatibility, we propose a matching way under the compatibility constraint and seek to recommend compatible items based on multi-modal compatibility. Finally, we performed a qualitative investigation on the fill-in-the-blank and fashion outfit compatibility tasks to evaluate the proposed method.