Towards a Virtual Garment Fitting Model for Male Upper Body for Online Marketplace

Hidellaarachchi, Dulaji; Hashini, Gunatilake,; Perera, Sandra; Sandaruwan, Damitha; Weerasinghe, Maheshya

doi:10.1109/icter.2018.8615576

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…The analysis shows that reliable fit & size information is the most important sub-indicator contributing to the customers' technology acceptance, which according to studies such as Hidellaarachchi et al (2018), Shen et al (2019), Misra et al (2018), Saarijärvi et al (2017), and Peng and Al-Sayegh (2014) and the approached online apparel retailers is indeed perceived as the apparel attribute with the most apparel returns. This proves that the Bayesian BWM is indeed a valid method to predict the importance of criteria.…”

Section: Discussionmentioning

confidence: 89%

“…This can mostly be seen by looking at the alternatives' weights with respect to the second and third most important sub-indicators affecting the users' technology preference which are 'the way of data collection through technology' and 'information handling by online clothing retailers' (see Table 4), implying that privacy and security concerns can occur when using A4 and A3. According to studies conducted by Hidellaarachchi et al (2018) and Apeagyei (2010) customer might perceive discomfort or privacy and security concerns regarding sharing personal body-measurements data and the way body-measurements data can be obtained and used by online apparel retailers. This is also the case for A4 and A3 with nearly all sub-indicators related to the perceived user-friendliness which are responsiveness, search time, availability and required preparatory work time, implying that these two state-of-the-art alternatives are perceived as the least user-friendly.…”

Section: Discussionmentioning

confidence: 99%

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Technology acceptance and return management in apparel e-commerce

Kalpoe

2020

Journal of Supply Chain Management Science

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Returns management, especially in apparel e-commerce, has gained increased attention due to the ecological and economic implications it imposes. However, research which explores the relationship between (i) reasons which drive customers' apparel returns and (ii) customer-based instruments designed to reduce online apparel returns, has not yet been empirically examined in literature, especially from the point of view of customers. This research aims to examine the customers' technology acceptance of four technological alternatives designed to prevent unnecessary apparel returns. To determine the customers' technology acceptance, the Technology Acceptance Model (TAM) is used. To operationalize TAM, a Multi-Criteria Decision-Analysis (MCDA) approach is applied, wherein the Bayesian group Best-Worst Method (BWM) is used to infer the weights of the indicators (i.e., criteria) that contribute to the customers'(users') technology acceptance. This is done within the context of apparel e-commerce and with the application of an online BWM survey and expert interviews. The results show that reliable fit & size information is the most important sub-indicator contributing to the customers' technology acceptance. Furthermore, it seems that whilst per subsequent alternative, the reliability of information provision regarding apparel attributes increases, the perceived user-friendliness (ease of use) of the technologies decreases, privacy and security concerns increase, and the managerial implications increase as well.

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Section: Discussionmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Technology acceptance and return management in apparel e-commerce

Kalpoe

2020

Journal of Supply Chain Management Science

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“…In order to prove that drawing the 3D garment pattern by the designer on the parametric model can not only save a lot of time for pattern-making, but also the final pattern-making effect can also satisfy the production requirement of the apparel industry, the strain maps of the virtual try-on 62,63 effect of the prototype based on 3D to 2D flattening drawn on the parametric model (PFPM) (Figure 20(a)), the prototype based on 3D to 2D flattening drawn on the scanned model (no human semantic information) (PFSM) (Figure 20(b)), and the New Bunka prototype (traditional pattern-making method) (Figure 20(c)) 64 have been compared in this section. As shown in Figure 20, the strain map shows the degree of deformation that occurred after the garment was worn on the virtual model.…”

Section: Resultsmentioning

confidence: 99%

A new parametric 3D human body modeling approach by using key position labeling and body parts segmentation

Cheng

Zeng

Bruniaux

et al. 2022

Textile Research Journal

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The current three-dimensional human models used in the apparel industry are mostly rigid and lack semantic information on body positions and body parts. Therefore, it is difficult for designers to make accurate, fast and effective designs from these models. This paper proposes a new parametric three-dimensional human body model based on key position labeling and optimized body parts segmentation. First, by using experts’ professional knowledge, we manually realize accurate human body data measurements as well as their interpretation and classification, and extract relevant human body features. After deep analysis, measured data irrelevant to body shape have been excluded by designers. Furthermore, the relation between body shapes and body features have been modeled. Second, based on this relational model, we label key positions on the corresponding three-dimensional body model obtained by scanning and segmenting the whole three-dimensional human body into semantically interpretable body parts. In this way, two databases have been created, enabling us to identify features of all segmented body parts, whose combination corresponds to the whole body shape. Third, for a specific consumer, his/her personalized three-dimensional human model can be obtained by taking a very few number of body measurements on himself/herself, making an appropriate combination of the identified body parts, and adjusting parameters of all involved body parts. By comparing the proposed labeled and segmented three-dimensional human model and the existing human models through a number of experiments, the proposed model leads to more relevant results with high accuracy and high visual quality related to real human body shapes.

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