Augmented Reality Fashion Show Using
Personalized 3D Human Models
Shihui Xu1(B) , Jingyi Yuan1 , Xitong Sun1 , Yuhan Liu1 , Yuzhao Liu1
Kelvin Cheng2 , Soh Masuko2 , and Jiro Tanaka1,

1 Waseda University, Kitakyushu, Japan{shxu,jingyyuan}@toki.waseda.jp, {sunxitong,liuyuhan-op,
liuyuzhao131}@akane.waseda.jp, jiro@aoni.waseda.jp
2 Rakuten Institute of Technology, Rakuten, Inc., Tokyo, Japan{kelvin.cheng,so.masuko}@rakuten.com

Abstract. When purchasing clothes online, consumers usually have to imagine
what they might look like when wearing them in real life surroundings. This canhinder their ability to make appropriate purchasing decisions. In order to alleviate
this uncertainty, we propose using augmenting fashion show, a new immersive
experience to enable consumers to create personalized 3D models of themselvesand have them fitted with a variety of purchasable clothing using Augmented
Reality. Furthermore, their models are enhanced with animations (walking and avariety of poses) depending on the real environment. In this way, consumers cansee a representation of themselves wearing a variety of clothes, and get a senseof what they would look like in real life surroundings. A prototype of our system
was demonstrated, and a preliminary evaluation was conducted to verify our system. We have received positive feedback in terms of effectiveness, assistance andpotential application.
Keywords: Augmented Reality · Virtual fashion show · Personalized humanmodel

1 Introduction
A fashion show is an event to showcase the new and upcoming clothing and accessories
designs, usually organized by fashion designers each season during a period known as
Fashion Week [1]. Typically, there is a runway with light and special effects decorated,
where models dressed in clothes designed by designers can walk the catwalk. With thewidespread adoption of ready-to-wear collections [2], ready-to-wear fashion shows are
becoming more and more diversified, with examples being held in street and supermarket
scenes [3], and promote the retail of fashion industry to a certain extent [2].
Despite the popularity of ready-to-wear fashion shows, consumers may still havedifficulty knowing whether the clothes are suitable for themselves or not. Clothes are
perfectly tailored at fashion shows, and appear at their best on the models. However,they may not be suitable for consumers themselves, because of the differences in body
© Springer Nature Switzerland AG 2020
S. Yamamoto and H. Mori (Eds.): HCII 2020, LNCS 12184, pp. 435–450, 2020.
https://doi.org/10.1007/978-3-030-50020-7_31

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shapes. In addition, it may be difficult for customers to imagine wearing these clothes
in their daily life. Some of the current fashion shows are not limited to the runway
or stage, instead the models are static, standing or sitting in an artificially constructedenvironment. But the constructed environment is still far from customers’ everyday lives.
Considering the above, we present an AR fashion show system in this paper, using
augmented and mixed reality. Our system enables users to have their own 3D models
which are customized for users and can reflect the appearance of the users (i.e. body
shapes and facial appearance). Virtual fitting room is provided for users, where users canselect the purchasable clothes virtually, and try them on their 3D human model. With
dressed 3D models, users can join in fashion show conducted in real-world environment.
Fashion show can be adaptively augmented to the daily life scenes where the users are.
In other words, according to real environment’s scene type (e.g. an office scene, a street
scene and a home scene) and spatial layout (e.g. size and shape of floor plane, number
and distribution of wall plane), the form of fashion show can vary, e.g. with different
postures or walking route.
In this way, we intend to offer ordinary customers immersive experience of attendinga ready-to-wear fashion show by themselves (Fig. 1). By using AR fashion show, we
provide customers with a preview of themselves wearing those fashion items in their daily
lives, which in turn can help them in their decision-making process when purchasing
fashion items. In addition, we have conducted a preliminary evaluation to validates our
system and received positive feedback in terms of effectiveness, assistance and potential
application.

Fig. 1. Our system provides users a way of joining in fashion show using personalized 3D humanmodels of themselves, making the real-world environment (left) an AR fashion show (right).

2 Related Works
We considered related works from three aspects, fashion show, personalized avatar andvirtual clothes try-on .

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2.1 Fashion Show
With the development of AR technology, many designers have thought about taking
AR into fashion show. For example, the autumn/winter 2014 fashion show by Goelia
had used AR technology to decorate runway [4]; Xenium Digital and Magnum enabled
virtual animals like tiger, panther and cheetah to walk together with models on runway
using AR [5]; Three launched mixed reality show where models are companied withspecial effects during catwalk [6].
In this paper, we consider how to leverage advances in AR and MR to make itpossible for ordinary customers to imagine themselves wearing the clothes that they are
considering purchasing in the form of a fashion show. In this vein, previous systems inhuman-computer interaction and computer graphics, such as the motion capture system
of Ryuzo Okada et al. [7] and the virtual fashion system of Stephen Gray [8], developed
virtual fashion show system that renders a computer graphics clothes to a 3D virtual
model. But these works lack of information of real world. In their works, fashion show
is isolated to real world, which means users have no ideas about the fashion show indaily life scenes.
2.2 Personalized Avatar
In the field of computer vision, a personalized avatar is a graphical representation of auser. Human avatar needs to be as realistic as possible.
There are several previous works explored generating personalized avatars for users.
Earlier work on personalized avatar were mostly based on parameter-driven modellingfrom images. Generally, 3D body modelling consists two parts: the representation of body
joints or skeleton structures, the body shape of the model. Gavrila [9] consider humanbody can be built using a 22-DOF model and the class of tapered super-quadrics [10].
Pascal Fua [11] proposed a framework which can form accurate body shape description
by a small number of parameters and have an effective use of stereo and silhouette data.
In recent years, 3D scanning technique are used to acquire the shape and pose ofa human body. Hasler [12] described an accurately model muscle deformations as afunction of pose. Silvia Zuff [13] proposed stitched puppet model which has the realismof 3D body models and the graphical structure of the graphical body model. The body partcan be described by low-dimensional state space and the whole parts will be connectedby pairwise potentials. Skinned Multi-Person Linear model (SMPL) [14] is a learned
model of accurate human shape and pose. Based on SMPL, Federica Bogo proposed
SMPLify [15], a way of automatic estimation of 3D human pose and shape from animage.
For our system, we employ the method of Alldieck [16] called video avatar which isbased on the parametric body model. Video avatar can infer highly accurate 3D model
shapes requiring only an RGB camera. To generate a 3D face model from a single facial
image, we use 68 feature key-points used the Dlib library.

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2.3 Virtual Clothes Try-on
With the development of computer graphics, virtual clothes models are becoming more
realistic. Earlier work on virtual try-on, mostly conducted in computer graphics [17–19]. Anna Hilsmann retextured garment overlay for real-time visualization of garments
in a virtual mirror environment [20]. Yamada et al. proposed a method of reshaping
the garment image based on human body shapes to make fitting more realistic [21].
However, like many other retexturing approaches they operate only in 2D without using
3D information in any way, which lacked the ability for users to view their virtual selffrom arbitrary viewpoints.
In recent years, some interactive 3D virtual try-on solutions have been reported. M
Yuan et al. presented a mixed reality system for 3D virtual clothes try-on that enables
a user to see herself wearing virtual clothes while looking at a mirror display [22].
Whereas, these kinds of system still do not allow users to see the textured garment fromarbitrary viewing angles.
Other virtual try-on system based on virtual avatar have been proposed, which enables
users to view the virtual garment from different angles. However, most virtual try-on
systems provide virtual fitting experiences on a default virtual avatar, rather than one
generated from user’s own body [23–25]. And some virtual try-on systems provide
virtual fitting experiences on a virtual avatar with user’s face [22] without personalizeduser body. The absence of “true fit” may disappoint customers when shopping online.
For our system, we propose to create virtual personalized models for each user,which can reflect their body shape and facial appearance, making the 3D virtual try-on
experience for users. Allowing user to view their virtual self with garment model fromdifferent viewpoints.

3 AR Fashion Show
Different from typical fashion shows, our purpose is to make a daily life fashion show
using users’ life-size 3D human models, which means it could adapt to the scenes whereusers are physically located. As shown in Fig. 1, our system aims to make the real-world
environment where the user virtual self become part of the fashion show using AR. Our
AR fashion show consists of three parts: scene preprocessing, immersive show and userinteraction, which will be introduced in detail below.
3.1 Scene Preprocessing
Scene means the real-world environment where the user is standing. To make a daily
life fashion show, we need to do some preprocessing to the daily life scene, including
recognition of scene and awareness of spatial layout (Fig. 2).
Recognition of Scene. Currently, scene information is seldom used as input in clothes
try-on system. While previous works had shown that the scene type has had a crucial
influence on outfits of customers [26, 27]. We propose to recognize the scene whereusers are and make it an input to AR fashion show system. For example, the scene type
could be office, street, supermarket and so on. With different scene as input, AR fashion
show is given different theme and form.

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Fig. 2. Scene preprocessing: recognition of scene type (left) and awareness of spatial layout(right).

Awareness of Spatial Layouts. To align virtual items to the real-world, spatial awareness system is needed. Our system scans the real environment through depth cameras
and understands the planes in real-world, and then decides the position of users’ models
in real-world according to the spatial layouts of real-world.
With scene preprocessing, AR fashion show can adapt to multiple scenes in real life,
which can facilitate users to have previews of wearing clothes in different daily scenes.

3.2 Immersive Show
Immersive show is provided for users in which users can see a 3D model of themselves
wearing the fashion items in AR and get some sense about taking a show. Generally,
most people watch fashion shows online or on TV, but few have the chance to watch
fashion show in person or take part in a fashion show. We therefore propose immersive
show, making the life scene a fashion show. With the personalized 3D human models ofusers, users can get a sense of taking a show. At the same time, they can also watch theimmersive show from various points of view.
The styles of immersive show contain dynamic walking and animated pose.
Dynamic Walking. During the immersive show, 3D human models can dynamicallywalk in AR environment, giving users a dynamic view of wearing fashion items on theirown body models while moving around.
The 3D models will find ways and walk around the real-world space. The walking
route is as the dotted line in Fig. 3. Wayfinding of model is based on the result of scene
preprocessing, so the ways may vary according to the scene change. From the result ofscene preprocessing, the max floor plane (green plane in Fig. 3) is found as the planewhere fashion show is superimposed. Next, to get the route, our system will assign four
points on the floor plane as the vertexes of rectangle, based on the position and size offloor plane. The points are the quarter points of max floor’s length or width. The foursides of defined rectangle will be the route of dynamic walking.
The postures of models’ dynamic walking are diversified and associated with thetype of scene. Usually, people walk differently in different occasions. To make thevirtual 3D human models walk naturally in various real-world scenes and give users arealistic feeling, we construct a library of walking animations for every user model. Every

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animation has its suitable application scenes. The walking animations contain catwalk,walking with bag, happy walking, texting while walking etc. According to scene typewe get from scene recognition, 3D models will be triggered to walk in different forms.
Figure 4 shows some examples of walking postures in an office scene. Figure 5 shows theexamples of walking postures in a street scene. In this case, users can better understandwhat they will look like walking in those daily life scenes.

Fig. 3. The route of dynamic walking is illustrated using dotted line with arrows. Route of dynamic
walking is defined by four points on the floor plane. (Color figure online)

Fig. 4. Dynamic walking: female catwalk walking, walking with bag and male happy walking.

Animated Pose. To make the fashion show more diverse and varied, we enable 3D
human model to make pose during the show. Animated pose is performed in the middleof dynamic walking, i.e. the second vertex of route rectangle, which is called the pose
point. When 3D human model walks to the pose point, it will stop to make an animatedpose. As the dynamic walking, a library of pose animations is also prepared for each 3D
human model. Models poses according to the current scene. For example, as the Fig. 6

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Fig. 5. Text while walking

shows, the animated pose ranges from looking around pose, female stand pose, waving
pose, saying hello pose and so on, which is relevant to our daily activities. The scene
shown in Fig. 6 is an office scene, so models will do the animated pose related to theoffice environment. This is designed for achieving a simulation of users’ daily actions
so that the AR fashion show is more relevant to people’s daily life.

3.3 User Interaction
Interactions are available to enhance the engagement of users, including the interactions
among multiple users and the interactions with environments.
Show Partner. Show partner is a multi-user interaction. AR fashion show systemenables users to invite another user as the partner of show. Show partner is designed
to give users some outfit inspirations about how to look good as a couple. Matchingstyles for couples have been popular in recent fashion industry. Users could be families,
friends or lovers, and want to select clothes for matching with each other. But currently,
users could not know how they and their partners will look like wearing matching styles
clothes. In light of this, show partner is provided for users to let them have a broad
understanding of the looks as a couple. Once a user is invited as a partner of anotheruser, the pair of users’ models will be assigned as show partners. Show partners will
walk together and make paired poses, as shown in Fig. 7.

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Fig. 6. Animated pose

Fig. 7. A pair of show partners.

Interactions with Models. Interaction with models directly is also made possible inour system. Users can give a “like” to any model by clicking the “like” button beside theAugmented Reality Fashion Show Using Personalized 3D

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models. The number of likes received will be shown on the left of every model’s likebutton, as shown in Fig. 8. Users can use hand gestures to interact with their own model
as well, including moving or rotating it.

Fig. 8. Like a model

4 Personalized 3D Human Model
Personalized 3D human model is generated for every user so that they can virtually try
on clothes and take part in AR fashion show using their own model. The generationof personalized 3D human model consists of the generation of 3D body shape modeland 3D face model. As the Fig. 9 shows, we generated the 3D body shape model ofuser using Alldieck et al’s method [16], and generated the 3D face model of users using
Deng et al’s method [28]. The two parts are then combined to get the complete user’s
personalized 3D human model.
4.1 3D Body Shape Model
3D body shape model of user is generated from a 2D video in which user rotates 360
degrees in front of a camera. To realize this, we use video from user as the input of thewhole system. To get the body model, we followed the method of Thiemo Alldieck [16].
We see human body modeling as part of preprocessing. After getting the video fromuser, the system will split the video into a series of frames. These frames can be used as444

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Fig. 9. Process of personalized 3D human model generation

input of preprocessing. Each frame will generate a corresponding mask using a method
called OSVOS [29]. This method can tackle the problem of semi-supervised video object
segmentation using a fully-convolutional neural network architecture.
Then we need to detect body joints data (such as human body, hand, foot key points)
using OpenPose [30]. Pose can be represented by many formats. We choose COCO as
pose output format which uses 18 key points to represent body joints. This information
will be stored into the json file. And each frame will generate a corresponding json file.
After getting the whole masks and key points files, we can use Alldieck’ work to
generate 3D human body model by SMPL model. Every SMPL model has two kinds ofparameters: pose and shape. We only care about the shape parameter because the system
will set every model to T-pose to rig the model. Alldieck’s work will calculate shapeparameter. Afterwards, the system will generate user’s body model as the output.
4.2 3D Human Face ModelingIn this part, we need to generate a 3D face model from a single facial image as the face ofthe 3D avatar of the user. To realize this, we first need a face image of the user. After that,the facial feature points of the image are required for modeling. To extract the featurepoints, here we used the Dlib library to get the 68 feature points of the user’s face. Fromthese points, we select the 5 points, the left eye, the right eye, the nose, the left mouthcorner and the right mouth corner and got their pixel coordinates for the latter use. After
getting these 5 points’ information, we followed Yu Deng’s work to generate the 3D facemodel of the user. In the process, we used the points information and the face image as
the input, and we could get the 3D face model.obj file as the output. In this way, we cangenerate the 3D human face model of the user.
We also need a hair model which is suitable for the user’s hair in order to makeour 3D avatar more realistic. We collected many hair models, and collected them as alibrary. From this library, the most similar hair model compared to the user’s haircut will
be chosen to generate the 3D avatar.
After having both the 3D body shape model and the 3D face model, we used Blender
to combine them together. Finally, the 3D user model will be served as the user’s avatar.

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5 Virtual Fitting
For virtual fitting, users can select clothes in the form of 2D images from shopping
website such as H&M. Then corresponding virtual clothes will be fitted on user’s personalized 3D human model. They can select the garments fitting in a real-life scene andview the virtual garment from various angles. The overall architecture of our system forvirtual try-on are shown in Fig. 10.

Fig. 10. The overall architecture of the virtual try-on

We gather various clothing design and information from websites, and map 2D
Garment images to generated 3D virtual garment templates, then match the clothes topersonalized body model of users.
5.1 Garment Model Generation
Our approach collects garment image information from existing shopping websites (e.g.
H&M [31], Zara [32]) to create a virtual garment library. Textures are extracted fromthe online garment image and mapped onto the 3D garment model in 3Ds Max.
Cloth weaver [33] is a blender garment template library which is used as the basis
for creating various garment models. We used Cloth Weaver to build several 3D garment
model templates for personalized human models. Some 3D clothing templates provided
to users are shown in Fig. 11. Such as short T-shirts, pants, skirts and long sleeves.

Fig. 11. Some 3D garment templates provided for users

We collect garment images on existing shopping websites such as H&M [31], ZARA
[32] …) and mapped these garment images onto the generated 3D garment model
templates in 3Ds Max. Some textured garment models are shown in Fig. 12 below.

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Fig. 12. Textured garment models

5.2 Virtual Fitting Room
In virtual fitting, users can select clothes in the form of 2D images from shopping website
such as H&M. The corresponding virtual clothes will be fitted on user’s personalized
model. The users’ personalized models wearing the virtual clothing will appear standingon the floor in front of them.
The animation of human body enables users to view the dressed human model ina dynamic and interactive way, from various perspectives. Therefore, users can have abetter understanding of whether the garment is suitable or not, while they move. Mostof the previous work focused on fitting on a static body model [19, 25]. So far, there isa lack of research exploring virtual try-on with motion. Therefore, we provide dynamic
interaction with the virtual human model in virtual fitting room.

6 Implementation
AR fashion show is developed using Unity 3D. We implemented our system on a seethrough type head-mounted display, Microsoft HoloLens, with Intel 32-bit CPU, 2 GB
RAM, and a 120° × 120° depth camera.
6.1 Scene Type and Spatial Layout
We used the Mixed Reality Capture API [35] provided by Microsoft to capture the photoof real environment. Then the captured photo is processed by using Google cloud vision
API [36] to get the labels of photo. The labels usually contain objects such as table,road, room and so on. The depth camera embedded in HoloLens is used to calculate thespatial layout of the real environment.
6.2 Libraries of Animations
To build the libraries of dynamic walking animations and animated pose animations,
we have used Maximo [37] from Adobe, which enables to empower various animations

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447with 3D characters. We classify the animations from Maximo into several sets according
to scene usage. In another word, we added the information of the scene to the animations
we get from Maximo to ensure the animations will be triggered correctly in different
scenes.

7 Preliminary Evaluation
We have conducted a user study to evaluate our system. The evaluation is conductedfrom the aspects of effectiveness, assistance and potential application.
7.1 Participant
Ten participants were recruited for this evaluation, including 7 males and 3 females aged
from 21 to 25. All of the participants have a background in human-computer interaction
with a basic knowledge of AR technology.
7.2 Task and Procedure
Each participant was asked to virtually try on clothes by the personalized 3D humanmodel which is generated by our system. Then using the dressed 3D models of themselves, participants can take part in an AR fashion show. During the show, they can watch
their models and interact with them.
The study was conducted in an office which was about 10 by 10 m.
After the show, participants were asked to complete a questionnaire to evaluate oursystem using 5-point Likert scale (1: strongly disagree to 5: strongly agree).
7.3 Results
We have asked 5 questions which covers the effectiveness, assistance and potential
application of our system. The results from our questionnaire reveals positive feedback
from our users. Table 1 shows the 5 questions and their average scores.
Effectiveness. Questions 1, 2 and 3 are designed to evaluate the effectiveness of oursystem. The effectiveness testing includes the effectiveness of personalized 3D humanmodel, the effectiveness of augmenting fashion show to real-world scenes and theeffectiveness of user interaction.
For question 1, we intend to know whether users can get a better understanding of
how the clothes would look on their body by using a virtual avatar. We see an averagescore of 4, indicating that the personalized 3D human model of our system can reflects the
personality of the user. We have gotten feedbacks about the photorealism of personalized
3D human model from participants. Most participants feel that body model part is moresimilar to the real person than the face model. We thought it might be because people are
more familiar with their face than their body shape, so that they could recognize quite
small differences between face models and real faces. But participants also referred thatsimilarity of body shape model is more important than face model during the selectingof clothes, so there is no problem if the face model has a few differences with real face.

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Table 1. Questions and corresponding scores
Question

Average score

1 I could get feelings about wearing clothes on my own virtual body by using 4
this system
2 I could get feelings about wearing clothes in real life scene by using thissystem3.5

3 I feel the user interactions are interesting and make me engage with theshow

4

4 I could make better purchasing decisions when buying clothes using thissystem

4.5

5 I would like to use the system when purchasing clothes in the future

4.5

From question 2, we intend to know whether users can get a better understandingof how the clothes would look like in a real-life scene. The average score for question
2 is higher than neutral value, suggesting that augmenting a fashion show to the realenvironment is effective for users to imagine the look in their daily life.
Question 3’s result shows that the user interaction can increase the amusement andengagement of AR fashion show experience.
Assistance. Question 4 is to evaluate whether our system can benefit customers, leading
to better decisions on the purchase of fashion items like clothes. The result is between
agree and strongly agree, indicating that our system do indeed help users in general.
Potential Application. Question 5 explored the application prospect of our system.
Result shows that our system has a lot of potential.
In general, the average scores of all questions were higher than 3 (neutral), which
implies that our system was generally viewed as positive in all aspects. This might verify
that our system designs are reasonable and practical.

8 Conclusion and Future WorkIn this paper, we present an AR fashion show system which uses personalized 3D humanmodels of users. Our system enables ordinary customers an immersive fashion show
experience within their real environment. A preliminary evaluation was conducted tovalidate our system. It demonstrated that our system is effective and can help customers
make better decisions on the purchase of clothes, having potential applications in future.
Since the fashion industry has the features of diversity and is rapidly changing, weneed to supplement or change the clothes models in time. In future, we hope to improve
the quality and quantity of clothes model in order to increase the universality of oursystem.

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