Consumer-centered collaborative design of fashion clothing brands: a communication and organizational structure study

Liu

Journal of Engineered Fibers and Fabrics

2021

The demand for fashion, and for virtual fitting and personalized fashion among customers, is changing the design and consumption of fashion. To meet such challenges, fashion design models are being developed based on big data and digitization, in which fashion is designed based on data, virtual fitting, design-support systems, and recommendation systems. This paper reviews the fashion design models proposed in recent years and considers future development directions for fashion design. Using big data and digital processing technologies, fashion designers identify the characteristics of popular fashions in the market, predict fashion trends, and create designs accordingly. The virtual fitting of scanatar, parametric mannequin, or even real human bodies, enables customers to quickly and easily find fashion that best meets their tastes and requirements. On consumer design-support platforms, consumers can freely select styles, colors, materials, and other fashion aspects and view the design output. Furthermore, fashion recommendation systems, guided by fashion design experts, have greatly improved consumer satisfaction with fashion design. Yet, current fashion design systems do not fully consider the performance of textile materials and do not involve functional fashion design, let alone comfort. Such limitations provide directions future research in fashion design.

Section: Fashion Recommendation Systemmentioning

confidence: 99%

Big data and digital design models for fashion design

Liu

Journal of Engineered Fibers and Fabrics

2021

Applied Mathematics and Nonlinear Sciences

“…Literature [13] proposes a fast-fashion apparel enterprise management approach based on radio frequency identification (RFID) technology, which aims to promote brand awareness and points out that there is a certain gap between the understanding of Chinese fashion brands and foreign fashion brands. Based on an all-around analysis of the organizational structure of Chinese fashion apparel brands, literature [14] learned that collaborative design promotes the significant improvement of the quality of design services, and the study provides important references for the image enhancement of Chinese apparel brands.…”

Section: Introductionmentioning

confidence: 99%

Research on clothing brand image design and communication under the SMCR communication model

2024

This paper utilizes the SMCR communication model to explore factors influencing clothing brand communication. We develop an evaluation model that assesses the effectiveness of these brands’ image communication, providing essential insights for brand design and communication strategy selection. The model employs a fuzzy comprehensive evaluation method for setting assessment criteria and an Analytic Hierarchy Process (AHP) to calculate the weight distribution of each index, forming a fuzzy affiliation judgment matrix. An evaluation system was constructed and applied to Chinese apparel brands, revealing that interest circles, online fashion bloggers, and video and live broadcasting websites are the top channels, accounting for over 50% of total brand image communication. In terms of preference, IP co-branded models, national fashion products, and tournament models are in the top three, accounting for 10.35%, 9.73%, and 8.86%, respectively. The comprehensive evaluation value of brand image communication effect in China’s apparel industry is 5.63, with a rating of “poor,” and there is still much room for improvement in apparel brand design and communication.

“…At present, the vast majority of clothing patterns design in China adopts repeated image reproduction, ignoring the innovation and fashion elements of clothing patterns. With the development of fast fashion brands in Chinese markets, customers begin to be attracted by the fashion clothing patterns of H&M, ZARA, and Uniqlo (Li et al , 2020). Patterns theme appears in fast moving consumer products every quarter, which is widely sought after by customers, and the patterns theme is also one of the main selling points for such big brands.…”

Section: Introductionmentioning

confidence: 99%

Research on clothing patterns generation based on multi-scales self-attention improved generative adversarial network

Luo

2021

IJICC

PurposeClothing patterns play a dominant role in costume design and have become an important link in the perception of costume art. Conventional clothing patterns design relies on experienced designers. Although the quality of clothing patterns is very high on conventional design, the input time and output amount ratio is relative low for conventional design. In order to break through the bottleneck of conventional clothing patterns design, this paper proposes a novel way based on generative adversarial network (GAN) model for automatic clothing patterns generation, which not only reduces the dependence of experienced designer, but also improve the input-output ratio.Design/methodology/approachIn view of the fact that clothing patterns have high requirements for global artistic perception and local texture details, this paper improves the conventional GAN model from two aspects: a multi-scales discriminators strategy is introduced to deal with the local texture details; and the self-attention mechanism is introduced to improve the global artistic perception. Therefore, the improved GAN called multi-scales self-attention improved generative adversarial network (MS-SA-GAN) model, which is used for high resolution clothing patterns generation.FindingsTo verify the feasibility and effectiveness of the proposed MS-SA-GAN model, a crawler is designed to acquire standard clothing patterns dataset from Baidu pictures, and a comparative experiment is conducted on our designed clothing patterns dataset. In experiments, we have adjusted different parameters of the proposed MS-SA-GAN model, and compared the global artistic perception and local texture details of the generated clothing patterns.Originality/valueExperimental results have shown that the clothing patterns generated by the proposed MS-SA-GAN model are superior to the conventional algorithms in some local texture detail indexes. In addition, a group of clothing design professionals is invited to evaluate the global artistic perception through a valence-arousal scale. The scale results have shown that the proposed MS-SA-GAN model achieves a better global art perception.