To provide a more realistic experience, e-retailers have implemented virtual try-on systems. It is, therefore, important to examine the variable that influences customers' intention to use try-on technologies when online shopping for apparel. The main aim of the current study is to identify and examine the design and individual characteristics that influence centennials to adopt virtual try-on systems. Factors extracted from the UTAUT2 model and technology readiness were proposed in the current study model, which was empirically validated based on data collected from 315 participants. The main results of structural equation modeling largely supported the significant role of "optimism" and "innovativeness" in performance expectancy and price value. Behavioral intention was also predicted by all the factors of UTAUT2 apart from effort expectancy. These results provide a guideline for online retailers on how to communicate with their centennial customers to influence them to adopt try-on technology.
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http://dx.doi.org/10.1016/j.ijinfomgt.2020.102254 | DOI Listing |
IEEE Trans Vis Comput Graph
July 2024
Digital garments are set to revolutionize the apparel industry in the way we design, produce, market, sell and try-on real garments. But for digital garments to play a central role, from designer to consumer, they must be a faithful digital replica of their real counterpart: a digital twin. Yet, most industry-grade tools used in the apparel industry do not focus on accuracy, but rather on producing fast and plausible drapes for interactive editing and quick feedback, thus limiting the value and the potential of digital garments.
View Article and Find Full Text PDFNeural Netw
August 2024
State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China.
Garment transfer can wear the garment of the model image onto the personal image. As garment transfer leverages wild and cheap garment input, it has attracted tremendous attention in the community and has a huge commercial potential. Since the ground truth of garment transfer is almost unavailable in reality, previous studies have treated garment transfer as either pose transfer or garment-pose disentanglement, and trained garment transfer in self-supervised learning, However, these implementation methods do not cover garment transfer intentions completely and face the robustness issue in the testing phase.
View Article and Find Full Text PDFErgonomics
March 2024
Ecole Nationale Superieure des Arts et Industries Textiles, GEMTEX Laboratory, Roubaix, France.
Garment pattern-making is one of the most important parts of the apparel industry. However, traditional pattern-making is an experience-based work, very time-consuming and ignores the body shape difference. This paper proposes a parametric design method for garment pattern based on body dimensions acquired from a body scanner and body features (body feature points and three segmented body part shape classification) identified by designers according to their professional knowledge.
View Article and Find Full Text PDFInt J Occup Saf Ergon
June 2024
School of Fashion, Wuhan Textile University, Wuhan, China.
Multimed Tools Appl
November 2022
REsearch Groups in Intelligent Machines (REGIM Lab), University of Sfax, National Engineering School of Sfax (ENIS), BP 1173, Sfax, 3038 Tunisia.
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.
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