A Virtual Personal Fashion Consultant: Learning from the Personal Preference of Fashion

Fu, Jingtian; Liu, Yejun; Jia, Jia; Ma, Yihui; Meng, Fanhang; Huang, H. Z.

doi:10.1609/aaai.v31i1.10536

Cited by 6 publications

(3 citation statements)

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“…Self‐investment affordance can be met by offering the chance to co‐create and customise a rental garment. Involving customers with virtual fashion consultants to rent garments based on their tastes and preferences can increase their investment in the renting process (Fu et al, 2017). Similarly, storytelling can be used to build rapport and actively associate Generation X with rented clothing.…”

Section: Theoretical and Managerial Contributionsmentioning

confidence: 99%

Gen Xers: Perceived barriers and motivators to fashion renting from the Indian cultural context

Jain

Singh

Jain³

et al. 2023

J of Consumer Behaviour

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Recently, the thought processes of consumers are evolving universally, making them reflective of the detrimental ecological footprints owing to excessive fashion consumption at a personal level. Consequently, they are increasingly thinking about accessing alternative, sustainable modes of consuming fashion. Furthermore, this transition is being witnessed across the generations and is not confined to any one generation. The current study pertains to the recent shift of “Generation X" towards fashion rentals. However, Gen X is considered less experimental, more conservative, and therefore less lucrative. To this end, the current study explores the barriers to Gen X's adoption of fashion rental and suggests motivations to promote it. It employs an interpretive qualitative research paradigm, obtains data from 32 Gen X fashion consumers via semi‐structured interviews, analyses the data using thematic analysis, and uses the theory of psychological ownership and the Hofstede cultural dimensions as an underpinning framework. Results indicate the emergence of six themes representing barriers, including loss of efficacy and effectance, loss of self‐identity and extended self, temporariness, environmental unsustainability, personal hygiene behaviour, and lifestyle. Similarly, motivations included three themes: long‐term subscriptions; transparency and assurances; and personalisation and storytelling. This implies that fashion rental firms need to embrace transparency and empower consumers through co‐creation, long‐term rental subscriptions, and the possibilities of possession. Further, the paper contributes to the identification of new dimensions, including a lack of nostalgia, risk avoidance, personal hygiene, an absence of personalisation, a dearth of education, emotional detachment, and voluntary simplicity in lifestyle. It also suggests future research.

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Section: Theoretical and Managerial Contributionsmentioning

confidence: 99%

Gen Xers: Perceived barriers and motivators to fashion renting from the Indian cultural context

Jain

Singh

Jain³

et al. 2023

J of Consumer Behaviour

View full text Add to dashboard Cite

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“…Another fundamental topic is clothing retrieval [2,[39][40][41], which can be used to find a specific clothes in an online shop using a picture taken in the wild. In addition to that, recommendation systems are also of great interest in order to suggest particular clothes based on the user's preferences [3,[42][43][44][45][46][47]. The task that is most relevant to this paper is that of synthesizing clothes images.…”

Section: Related Workmentioning

confidence: 99%

Would Your Clothes Look Good on Me? Towards Transferring Clothing Styles with Adaptive Instance Normalization

Fontanini¹,

Ferrari²

2022

Sensors

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Several applications of deep learning, such as image classification and retrieval, recommendation systems, and especially image synthesis, are of great interest to the fashion industry. Recently, image generation of clothes gained lot of popularity as it is a very challenging task that is far from being solved. Additionally, it would open lots of possibilities for designers and stylists enhancing their creativity. For this reason, in this paper we propose to tackle the problem of style transfer between two different people wearing different clothes. We draw inspiration from the recent StarGANv2 architecture that reached impressive results in transferring a target domain to a source image and we adapted it to work with fashion images and to transfer clothes styles. In more detail, we modified the architecture to work without the need of a clear separation between multiple domains, added a perceptual loss between the target and the source clothes, and edited the style encoder to better represent the style information of target clothes. We performed both qualitative and quantitative experiments with the recent DeepFashion2 dataset and proved the efficacy and novelty of our method.

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“…Many research efforts have been made in this domain, focusing on clothing recognition, 2–5 clothing retrieval, 6–9 clothing parsing, 10–12 clothing collocation, and recommendation. 13–16 We therefore focus on the research areas of clothing collocation. Clothing collocation measures whether a group of clothing items collocate with one another.…”

Section: Related Workmentioning

confidence: 99%

Clothing Color Collocation with Deep Neural Networks

Zhang

Yue

et al. 2021

AATCC Journal of Research

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Color is the foundation of well-put-together ensembles in clothing collocation. The way people coordinate and match different hues within looks may bring different visual feelings. However, clothing color collocation is an extremely challenging task due to great changes of public tastes and differences of consciousness towards color collocation. We propose a clothing color collocation model that consists of two deep neural networks based on color histograms calculated from a quantized color space and design features from clothing images. Given a clothing collocation, the model produces a score that indicates the degree that these clothes match. Experiments on Polyvore409k data sets validate the proposed model for clothing color collocation.

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A Virtual Personal Fashion Consultant: Learning from the Personal Preference of Fashion

Cited by 6 publications

References 0 publications

Gen Xers: Perceived barriers and motivators to fashion renting from the Indian cultural context

Gen Xers: Perceived barriers and motivators to fashion renting from the Indian cultural context

Would Your Clothes Look Good on Me? Towards Transferring Clothing Styles with Adaptive Instance Normalization

Clothing Color Collocation with Deep Neural Networks

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