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Nowadays, artificial intelligence (AI) technology has demonstrated extensive applications in the field of art design. Attribute editing is an important means to realize clothing style and color design via computer language, which aims to edit and control the garment image based on the specified target attributes while preserving other details from the original image. The current image attribute editing model often generates images containing missing or redundant attributes. To address the problem, this paper aims for a novel design method utilizing the Fashion-attribute generative adversarial network (AttGAN) model was proposed for image attribute editing specifically tailored to women’s blouses.

The proposed design method primarily focuses on optimizing the feature extraction network and loss function. To enhance the feature extraction capability of the model, an increase in the number of layers in the feature extraction network was implemented, and the structure similarity index measure (SSIM) loss function was employed to ensure the independent attributes of the original image were consistent. The characteristic-preserving virtual try-on network (CP_VTON) dataset was used for train-ing to enable the editing of sleeve length and color specifically for women’s blouse.

The experimental results demonstrate that the optimization model’s generated outputs have significantly reduced problems related to missing attributes or visual redundancy. Through a comparative analysis of the numerical changes in the SSIM and peak signal-to-noise ratio (PSNR) before and after the model refinement, it was observed that the improved SSIM increased substantially by 27.4%, and the PSNR increased by 2.8%, serving as empirical evidence of the effectiveness of incorporating the SSIM loss function.

The proposed algorithm provides a promising tool for precise image editing of women’s blouses based on the GAN. This introduces a new approach to eliminate semantic expression errors in image editing, thereby contributing to the development of AI in clothing design.

One of the main challenges that brands face is the ability to provide a real-life experience through online platforms. The aim of this study is to analyze consumers' self-concept and testimonials through an augmented reality (AR) try-on app versus a website.

An online survey was conducted with 222 participants, in which they were randomly exposed to two scenarios: AR try-on app or website experience and positive versus negative reviews presence.

The findings indicate positive effects of ideal self-congruence on purchase intention and confidence in fit. Low self-esteem consumers experience greater ideal self-congruence using AR when compared to the website. Confidence in fit partially mediates between ideal self-congruence and purchase intention. Higher levels of diagnosticity of reviews conduct to higher levels of purchase intention.

This study contributes to interactive marketing by exploring the effect of appearance self-esteem in the relationship between AR app versus website and ideal self-congruence and how this influences consumer behavior. In addition, the study also emphasizes the role of testimonials in shaping consumer intentions.

Accurate and automatic clothing pattern making is very important in personalized clothing customization and virtual fitting room applications. Clothing pattern generating as well as virtual clothing simulation is an attractive research issue both in clothing industry and computer graphics.

Physics-based method is an effective way to model dynamic process and generate realistic clothing animation. Due to conceptual simplicity and computational speed, mass-spring model is frequently used to simulate deformable and soft objects follow the natural physical rules. We present a physics-based clothing pattern generating framework by using scanned human body model. After giving a scanned human body model, first, we extract feature points, planes and curves on the 3D model by geometric analysis, and then, we construct a remeshed surface which has been formatted to connected quad meshes. Second, for each clothing piece in 3D, we construct a mass-spring model with same topological structures, and conduct a typical time integration algorithm to the mass-spring model. Finally, we get the convergent clothing pieces in 2D of all clothing parts, and we reconnected parts which are adjacent on 3D model to generate the basic clothing pattern.

The results show that the presented method is a feasible way for clothing pattern generating by use of scanned human body model.

The main contribution of this work is twofold: one is the geometric algorithm to scanned human body model, which is specially conducted for clothing pattern design to extract feature points, planes and curves. This is the crucial base for suit clothing pattern generating. Another is the physics-based pattern generating algorithm which flattens the 3D shape to 2D shape of cloth pattern pieces.

In recent years, there has been significant interest in adopting XR (extended reality) technologies such as VR (virtual reality) and AR (augmented reality), particularly in retail. However, extending activities through reality-mediation is still mostly believed to offer an inferior experience due to their shortcomings in usability, wearability, graphical fidelity, etc. This study aims to address the research gap by experimentally examining the acceptance of metaverse shopping.

This study conducts a 2 (VR: with vs. without) × 2 (AR: with vs. without) between-subjects laboratory experiment involving 157 participants in simulated daily shopping environments. This study builds a physical brick-and-mortar store at the campus and stocked it with approximately 600 products with accompanying product information and pricing. The XR devices and a 3D laser scanner were used in constructing the three XR shopping conditions.

Results indicate that XR can offer an experience comparable to, or even surpassing, traditional shopping in terms of its instrumental and hedonic aspects, regardless of a slightly reduced perception of usability. AR negatively affected perceived ease of use, while VR significantly increased perceived enjoyment. It is surprising that the lower perceived ease of use appeared to be disconnected from the attitude toward metaverse shopping.

This study provides important experimental evidence on the acceptance of XR shopping, and the finding that low perceived ease of use may not always be detrimental adds to the theory of technology adoption as a whole. Additionally, it provides an important reference point for future randomized controlled studies exploring the effects of technology on adoption.

This study aims to investigate the influence of different virtual fashion styles on attitude and satisfaction within virtual reality (VR) tourism experiences. The investigation considers the mediating effect of perceived attractiveness, popularity, novelty and weirdness, as well as the moderating role of self-congruence with avatar clothing and the desire for unique products.

This research uses a quantitative experimental approach. Initially, a three-step pilot study (N = 201) was conducted to select avatar fashion styles for the main investigation. In the primary study, participants (N = 326) engaged with one out of four fashion style conditions to select attire for their avatars and then completed a self-administered survey. Data analysis involved paired-sample t-tests, multivariate analysis of variance and Hayes’ PROCESS Models.

The results show that presenting fantasy avatar fashion styles leads to a decrease in perceived attractiveness and popularity, while concurrently increasing perceptions of novelty and weirdness which in turn exert a negative influence on attitude and satisfaction with the virtual fitting room (VFR). However, these relationships change when considering the moderating role of self-congruence with avatar clothing and the desire for unique products.

VR tourism experience providers and designers can use research findings to bolster positive attitude and enhance satisfaction with VFR; an important first step that strongly affects the rest of the VR tourist journey.

This study contributes to tourism research by exploring the intersection of immersive technologies and virtual fashion. It emphasizes the enhancement of critical touchpoints like the VFR, moving beyond a sole focus on VR adoption, to improve the overall virtual tourist experience.

The purpose of this study is to investigate the effects of visual and verbal information presentations on mental imagery, perceived informativeness and purchase intention. The study assesses two types of product-related information: (1) visual information: static product images and augmented reality (AR) and (2) verbal information: abstract and concrete product reviews.

A total of 320 mobile consumers participated in the experiment. To increase external validity, this study was conducted in an existing digital shopping environment.

The results suggest that AR has a greater effect on consumers' shopping outcomes than static images. The findings further reveal that concrete product reviews are important in increasing mental imagery, perceived informativeness and purchase intention when visual information does not provide an AR function.

This study contributes to the current literature by providing empirical support for AR effects and concrete reviews on consumer responses. The results further provide an important perspective for retailers seeking ways to develop effective information presentations in digital retailing.

The purpose of this paper is to examine the factors that influence older adults' intention to use virtual fitting room technology during the COVID-19 pandemic based on the extended technology acceptance model (TAM).

An online survey was conducted with a sample of older adults from 60 to 90 years old (n = 819). A structural equation modeling was conducted to test a proposed research model.

The results revealed that older adults' behavioral intentions were positively influenced by perceived usefulness and ease of use, and fear of infection during the pandemic was significantly related to the perceived usefulness. Fit concern was not significantly related to perceived usefulness of virtual fitting room technology.

This research extends the TAM by adding antecedents to perceived usefulness in explaining older adults' adoption of virtual fitting technology.