LITERATURE REVIEW: VIRTUAL FITTING ROOM
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Table of Contents
Literature Review: Virtual Fitting Room 3
Introduction 3
Virtual Fitting Rooms 3
How VRF can be effective for fashion retailers (clothes only) in STORES 5
Future Trend of VFR in Physical Fashion Stores 6
Technologies behind virtual fitting room (VFR) 8
How virtual fitting rooms works 8
3D Customer’s Model 8
3D Body Scanning 9
Photo Accurate VFR 12
Size Recommendation Services 12
3D Augmented Reality for VFR 13
Pros and Cons of the Technologies in the Market for Physical Stores 15
Brands That Use the Technologies 16
Summary 17
References 18
Literature Review: Virtual Fitting Room
Introduction
This chapter provides a detailed review of previous works related to virtual fitting rooms for physical fashion stores. As technology permeates into different aspects of the modern business environment, physical stores are also under pressure to advance their services and leverage the opportunities offered in the virtual space (Huang et al., 2019). Huang et al. (2019) observe that, in traditional stores, shoppers mainly interacted with products physically, using senses such as taste, smell, sight, hearing and touch before making purchases. The approach was more advantageous than the virtual e-commerce shopping experience where misfits are commonly experienced. However, given the popularity of e-commerce websites and fashion stores, the physical fashion stores are losing more customers hence emphasizing on the need to adopt novel strategies that ensure they survive in the market.
Technology is solving the challenge through the introduction of virtual fitting rooms as a replacement of classic dressing rooms. A survey by Gartner (2019), reiterates the finding by estimating that about 100 million consumers are expected to shop in augmented reality online and in-store outlets by 2020. Virtual Fitting Rooms (VFRs) as described by Nguyen, Hoang and Jȩdrzejowicz (2012) refer to unique systems that allow users to simulate fitting clothes on avatars in order to explore both size and fit and as a result, facilitate decision-making. As such, VFR is an immersive technology that enhances the customer’s shopping experience by ensuring simplicity and comfort. Higgins et al. (2018) posit that the introduction of VFRs in physical stores will play a significant role in enabling the customers to virtually try on multiple clothes based on their preferences in an efficient way in this digital era. This chapter is divided into sections including the definition of virtual fitting rooms (VFR), how it works, and its effectiveness in the physical fashion stores, technologies used to support VFR, their advantages, future trends of the technology and identify some example of brands where the VFR has or is being implemented.
Virtual Fitting Rooms
A virtual fitting room is a system designed to allow individuals to try out digitally various apparel on a customized 3D (three-dimensional model) before the person decides to purchase it (Kaur, 2014). The devices used to execute the computing services of VFR applications have a unique user interface created to capture the values of several parameters related to the corresponding measures of the individual’s body parts. In most instances, server applications interconnected to the computing devices utilize the parameter values collected to create a graphical 3D model fitting particular individual. According to Curry and Sosa (2017), focused on its application in the clothing sectors, three-dimensional scanning is performed on the individual’s body to obtain accurate representations of the individual in minimal clothing and properties, also known as standard garments.
For a user to select new apparel or garment whose properties are known, the user interfaces play a major role in allowing them to access the database of accessories and garments. Moreover, finite element analysis is performed for determining the shape of the users’ full-body, and apparel and the more suitable visual display of the selected accessory that is proportionately equal to the body of the user on the assessment are created (Curry and Rosa, 2017). Triwahyuningrum et al. (2015) argue that virtual fitting rooms are not only used to determine whether the clothes fit the body of the consumers but also other preferences such as colour. These features provide the customers with a better in-store shopping experience by enabling them to choose clothes suiting individual preferences.
According to Gültepe and Güdükbay (2014), there has been significant research on the subject of virtual fitting rooms for over a decade. As cited by Gültepe and Güdükbay (2014), previous researchers created an internet-based mechanism of VFRs. However, it had weaknesses in operating in real-time since the mechanism needed marker-based motion capture systems to perform animations. Nevertheless, Triwahyuningrum et al. (2015) argue that virtual fitting rooms comprise three major phases, which include “motion detection, determination of the region of interest of the detected motion, superimposed the virtual clothes into the region of interest” (p.1446).
The motion detection stage utilizes the double-difference algorithm because the algorithm does not require a reference frame. Additionally, the algorithm works by using the initial and next frame in detecting the motion of the new frame. To assess the algorithm performance accuracy, data in the Perception Test Images Sequences Dataset are employed in experimentation. Based on the proposed Madura Batik VFR, the algorithm performance accuracy indicates above-average values for balanced accuracy, specificity and sensitivity. Figure 1 below illustrates the motion detection phase.
Figure 1: Motion detection – Left frame, right frame and detected motion (Triwahyuningrum et al., 2015)
In the second phase, the region of interest of the detected motion is determined by evaluating the entire pixels that are recognized as the foreground image or moving pixel. Figure 2 illustrates the identification of the region of interest.
Figure 2: Region of interest of the detected motion (Triwahyuningrum et al., 2015)
The third phase involves superimposing the virtual clothes onto the region of interest. In this phase, the virtual clothes are moved along with the location of the consumer’s body thereby, embedding them onto the region of interest. Figure 3 below shows superimposing of the virtual clothes.
Figure 3: Superimposing virtual clothes onto region of interest (Triwahyuningrum et al., 2015)
As this section introduces the concept of VFR and describes how they work, the discussion on their effectiveness in stores is discussed in the next section.
How VFR can be effective for fashion retailers (clothes only) in STORES
This section examines how VFR will be effective for future fashion retailers. According to Pham (2015), virtual fitting rooms can be a significant solution in the fashion industry, specifically addressing the challenges of misfits and negative perceptions that existed traditionally. The VFRs are designed to be embraced by consumers compared to other scanning technologies. Being the latest technology in the fashion industry, VFRs, they are connected for purposes of expanding the surveillance culture in different countries. Moreover, Pham (2015) argues that technology will establish an obscure racial ideology behind fashionability judgments. Therefore, VFRs are vital in rationalising and systemising the cultural beliefs that have been held for years under cover of colour blindness associated with technology. Similarly, Dawndasekare et al. (2016) note that three-dimensional virtual rooms employ computer-generated 3D images in creating avatars of the consumer similar to that individual. Moles (2015) also argue that the avatars created will be designed with personalised features such as skin tone, utilisation of different hairstyles. The customer’s face image can also be used in creating a more custom avatar. Therefore, through the measurements collected from the customer, the avatar can be used to show how particular clothing selected by the customer will fit them.
According to Blázquez (2014), the store experience is influential in generating value perceptions among consumers in retailing. The researcher argues that the integration of interactive technologies such as VFRs within the retail stores creates value for them and positively influences their purchase decisions. Similarly, Lee and Xu (2019) state that VFRs offers great opportunities for the fashion sector by helping the customer to try on products virtually. Therefore, VFR transforms the customers experience from physically trying on clothes to enabling them to use simulations of different clothing design to find the most suitable outfit. The next section discusses the application of VFR technology in physical fashion stores.
Future Trend of VFR in Physical Fashion Stores
Future trends show the potential creation of more custom applications that offer users opportunities to choose preferences to fit clothes in lieu of changes in the marketplace. For instance, according to Higgins et al. (2018), users in their virtual market will be able to create virtual closets for their items according to the preferences in the outfits. Additionally, VFRs are likely to be integrated into social media networks such as Twitter and Facebook to enables users to showcase their virtual outfits in virtual fashion shows. As a result, the companies operating in the market have to extend their services to accommodate the changes offered by virtual reality. On the other hand, Sekine et al. (2014) suggest that the new virtual systems are being created for seamless adjustments of two-dimensional clothing pictures to consumers who infer their three-dimensional body shape models. Therefore, the systems can transform and overlay the clothing images onto the image of the body in real-time.
Sekhavat (2016) argues that the production of three-dimensional models for humans moving in real-time is a daunting task requiring several sensors. Hence, new three-dimensional models such as multiple-Kinect capturing systems were proposed although they have been identified to be ineffective as mapping of the cloth textures on moving humans or objects is a complex activity. According to Sekhavat (2016), the proposed systems also need fitting rooms to be equipped with multiple Kinects, thus affecting the users’ convenience. Besides, when applied to stand users, the customers can easily view their images on large televisions wearing the clothes. This approach takes the image of users, and multiple cloths are augmented on the image. Through the evaluation of this approach, Sekhavat (2016) argued that there are challenges in setting up VFRs during the live augmented reality try-on processing of the images. On the contrary, Ziquan and Zhao (2012) argue that Kinect is the most suitable tool required in motion sensing input devices to monitor the gestures, poses, and positions of users. Besides, Kumari and Bankar (2015) agree with the claims of Sekhavat (2016) about the weaknesses of Kinect by noting that Kinect sensors are expensive, and web cameras can be used as effective alternatives.
Beck and Crie (2018) note that VFRs influences the customer’s intentions of trying on and visualizing products. However, VRFs are not widely used by the customers due to the concerns associated with the accuracy of simulations (Lee and Xu, 2019). The argument advanced is that, as VFR empowers shoppers within brick and mortar stores to try out and match different outfits, they are able to make more informed purchase decisions in the fashion stores as compared to traditional setups where misfits were identified (Pham, 2015). Therefore, the transformation is associated with the ability to visualize outfits on 3D immersive platforms as opposed to trying out the outfits before adoption. Additionally, the existence of multiple VFR technologies in the market having unique capabilities and using varied solutions will hinder the adoption process for retailers the most suitable technology for their businesses.
Technologies behind virtual fitting room (VFR)
How virtual fitting rooms works
The basis of virtual fitting rooms is the utilization of motion detection algorithms that are commonly used in applications for tracking objects. Triwahyuningrum et al. (2015) argue that the main aim of the motion detection algorithm in VFRs is to determine the positions of users in all the frames of the data video. For the algorithm to perform more effectively, the first frame is required to be empty. However, the double differences algorithms have a challenge since one frame can be delayed from the actual system (Triwahyuningrum et al., 2015). On the other hand, according to Higgins et al. (2018), once the users in the virtual fitting room, apart from trying on virtual items such as clothes, they have an opportunity of dressing virtual outfits to their closet.
Virtual fitting rooms employ different techniques of operations for standing and moving customers. For instance, Kumari and Bankar (2015) argue that Kinect sensors and web cameras are used to design clothing while the consumer is on the move. Other VFRs are designed to select personalised clothing based on the measures collected from the customer standing in front of a camera. According to Giovanni et al. (2012), some VFRs such as EON Interactive Mirror is designed with incorporation of the high-definition camera (RGB camera) and Kinect sensors to capture and simulate images tilted up and down in different angles of -27 to +27 degrees. Van Hoof et al. (2016) argue that the sensors used in the VFRs take the consumer’s measurements in the fitting room, including the skin tone, hair colour, mood, complexion, gender, then selects best-fit garments to meet the demands of the customer.
3D Customer’s Model
The advances of virtual fitting rooms will contribute to the development of a 3D customer model. Rayna and Striukova (2016) aver that the majority of the designers and companies will be able to sell three-dimensional models of their fashion products directly to the consumers. Therefore, users will take advantage of the benefits offered by the customer’s model to change their body shapes, create three-dimensional versions of themselves based on their measurements or information collected from the scanning devices. 3D simulation of clothes shows the appearance and types of fabrics utilized throughout the dynamic process. For example, Figure 4 shows the satin fabrics interaction with a ball (a) and the simulation results (b).
Figure 4: 3D dress simulation (Boonbrahm et al., 2015)
Advantages and disadvantages: the main advantage of this approach is fostering the future trends of innovation in VFRs. According to Moles (2015), the technique can be modified to change the shapes of bodies, and it offers the customer, opportunities for uploading their facial images for personalising outfits. However, the major downsides of technologies based on this model do not automatically select clothing but instead relying on input data collected by either scanning devices from the customer.
Photo Accurate VFR
The main advantage of this technology is due to the combination of mix and match (dress-up mannequins) and real models (Moles, 2015). Based on the study of Upadhyay et al. (2017) and focused on the analysis of virtual trial dressing and makeover and the study of Erra et al. (2018), photo-accurate VFR was identified as one of the technological approaches implemented in VFRs. The approach involves taking pictures of robotic mannequins that are dynamically changing with dimension and shape is smaller levels. Hence, once users enter measurements corresponding to the particular body, the images fitting the measurements are displayed. Depending on the shape and size of the body, photo-accurate technology ensures that dynamic changes results in the selection of clothes fitting the specifications of the customers as shown in figure 5 below.
Figure 5: Photo-accurate VFR simulation (Sekine et al., 2014)
Advantages and disadvantages: The Pros of this technology include; employing sophisticated robotic technology with dynamic measures, which ensures that the customers get the best outfits matching their input information. According to Prasad, Kavya, and Devi (2019), the technology is highly focused on the accuracy of selected outfits. However, this technology has challenges of reliance on the database, where customers may lack outfits since the dimensions provided do not match ones stored in the database (Upadhyay et al., 2017).
Size Recommendation Services
According to Moroz (2019), size recommendation services are conventional approaches implemented in VFRs. Similar to the Photo-accurate VFR technology, the size recommendation services involve the utilisation of a computer-controlled system with dynamic measurements based on the user input values or image uploaded. Size recommendation software applications such as Fitizzy and Fit Analytics have been created for e-commerce businesses to provide their clients’ detailed sizing informing and enhance the purchasing experience through informed decision making (G2, 2020). Similarly, Randall (2015) argues that size recommendation services are used to simulate the physical fattiness and sizing experience. According to Randall (2015), users can load their size details to the VFR which then recommends suitable clothing designs (Figure 6).
Figure 6: VFR size recommendation system (Randall, 2015)
Advantages and disadvantages: they perform well when the dimensions are already stored in the database, and they enable physical stores to offer customers with sufficient information to select the most suitable clothing (G2, 2020). However, the size recommendation systems one downside, which is the error messages displayed in case the user inters body measurements not already stored in the databases (Randall, 2015). This may demotivate customers from using VFRs.
3D Augmented Reality for VFR
Pachoulakis and Kapetanakis (2012) examined the platforms of augmented reality (AR) for VFRs. They noted that virtual fitting rooms highly rely on AR that utilised specialised hardware and software in merging the physical and virtual worlds through immersion of digital information into real-world videos for generating attractive scenes in real-time. Pachoulakis and Kapetanakis (2012) argue that the platforms created to provide the real-time simulation of videos to enable the consumers to visualise the products in the current outfits and check the outlook from several different angles. As shown in figure 4, the technology enables the customers to try on different clothing types including skirts, jeans and others. On the other hand, a study conducted by Bonetti, Warnaby, and Quinn (2016) states that AR technology and virtual reality are emerging technologies adopted both in physical retailing to enhance the shopping experience and selling environment.
Figure 7: 3D augmented reality simulations (Pachoulakis and Kapetanakis, 2012)
According to Scholz and Smith (2016), AR is currently being implemented in multiple sectors to display digital transformation over a real-time view of space and objectives by people in the actual world. Besides, technology can be a significant contribution if integrated into marketing programs as it has the potential of maximising the customer’s engagement. However, when applied in virtual fitting rooms, the customers be influenced to avoid engaging AR experiences in the streets since passers-by could see their “strange movements” (Scholz and Smith, 2016, p.6). Similarly, Yaoyuneyong, Foster, and Flynn (2014) examine the issues affecting the efficacy of AR virtual fitting room technology where the researchers noted that web irritation informativeness, entertainment value, economic motivation, and the consumer’s innovation capabilities are the main influences of the customer’s intentions to use virtual dressing technology.
Advantages and disadvantages: The pros include; this technology can be integrated with virtual reality to create an efficient and accurate simulation to improve the in-store shopping experience of customers. According to Scholz and Smith (2016), AR models can be designed to maximise user engagement. Moreover, AR is more effective than the user’s webcams in terms of the depth of the sensing cameras (Zugara, 2017). On the other hand, the cons of AR include high costs for retails to produce 3D assets for the clothes, and not all customers recognise the value added by AR in their in-store shopping experience.
Brands That Use the Technologies
According to Bonetti et al. (2016), one of the brands which utilised augmented reality to improve the customer’s shopping experience is the Zugara e-commerce solution through the “Webcam Social Shopper”. The solution enables the customers to “hold” various clothes against themselves and examine the outfits while wearing them in real life (Zugara, 2015). Zagel (2016) states that some brands used mobile applications equipped with AR technologies in stores where the applications are utilised in fitting purposes and providing mirrors that are enhanced digitally as well as offer personalised recommendations together with the locations for the customers. For example, the UK fashion retailers such as Bloomingdale implemented AR technology that enables its customers to walk past the stores try their products virtually in the streets. At the same time, TopShop used AR-supported virtual fitting rooms for specific stores for customers to try on the company’s products (Bonetti et al., 2016).
Summary
This chapter focused on reviewing different literature materials of issues associated with virtual fitting rooms. Based on the analysis of the findings from this review, it is evident that virtual fitting rooms have been widely adopted. The analysis indicates that although brick-and-mortar stores are challenged in delivering enhanced customer experiences, 3D immersive technology is considered an effective solution to the problem through the integration of VFRs. Likewise, the review showed that implementation of VFRs in physical fashion stores, using cameras and advanced tools such as Kinect, transformed the shopping experience by enabling customers to try out different sized outfits and in effect, avoid mismatches. Directly, this translates to higher savings for the stores as they reduce expenditure incurred in developing traditional fitting rooms. VFRs improve the customer’s shopping experience within physical stores as they are able to choose multiple outfits, obtain the most suitable suggestions based on their measurements, and try out clothes virtually. However, the VFR technology are limited by several issues, one of which was the concern by customers regarding the accuracy of simulations. Nonetheless, based on the highlighted evidence, the performance of physical stores is expected to be improved significantly through the integration of VFRs as they directly enhance the shopping experience.
References
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