Image-Based Virtual Try-On System with
Garment Reshaping and Color Correction
Yoshihiro Kanamori, Hiroki Yamada, Masaki Hirose,
Jun Mitani, and Yukio Fukui
University of Tsukuba, Japankanamori@cs.tsukuba.ac.jp,{yamada,hirose}@npal.cs.tsukuba.ac.jp,{mitani,fukui}@cs.tsukuba.ac.jp
http://www.npal.cs.tsukuba.ac.jp/

Abstract. We propose an image-based virtual try-on system for garments in order to reproduce the appearance of try-on during online shopping. Given whole-body images of a fashion model and a customer, we
cut out a garment image from the fashion model image and reshape it
so that it fits to the body shape of the customer. The reshaping function
is estimated automatically from the body contours of the fashion modeland the customer. The fitting result is refined further by automatic color
correction with reference to the facial regions and a method of retouchingparts that protrude from the rear of the garment image. We verified theeﬀectiveness of our system through a user test.
Keywords: image-based virtual fitting, image warping, color constancy.

1

Introduction

The number of users shopping online for garments has tended to increase recently, and this is expanding widely as means of enjoying shopping comfortably
from customers’ own homes. However, there is a problem that we cannot try
on clothes because we cannot handle real garments, unlike shopping in a real
bricks-and-mortar store. Since fit is such an important criterion when buying
garments, this is one reason impeding the spread of online shopping. To address
this problem, there have been proposals for virtual fitting systems that implement fitting online in a virtual manner. For example, the online virtual fitting
service Awaseba [6] composites a whole-body image of a registered customer(hereafter called a “customer image”) with an image of a garment from a catalog (hereafter called a “garment image”), and presents an image of the customer
apparently wearing the garment. However, typical online services just provide
virtual fitting by naı̈ve compositing.
Virtual fitting by naı̈ve compositing often produces unnatural images (Figure 1, right). Whereas this is partially because of the diﬀerences in brightness
between the images, the large cause is that the shape of the garment image
does not match the body shape of the customer. For example, in Figure 1, the2Y. Kanamori, H. Yamada, M. Hirose, J. Mitani and Y. Fukui

Fig. 1. Virtual fitting by a naı̈ve superimposing. From left, the garment image, thecustomer image, and the fitting result. Naı̈ve superimposition results in an unnatural
image mainly because the garment does not match the body shape.

garment is neither matched to the positions of the arms nor the width of thetrunk.
We therefore introduce an image-based virtual fitting system that suppresses
the unnaturalness of the virtual fitting image. In our system, the garment image
to be fitted is cut out from an image of it being worn by a fashion model, andthen is warped according to a warping function with which the body shapeof the fashion model matches that of the customer. This can be considered
as warping in the literature of deformation transfer [18], where deformation istransferred from a source 3D mesh onto another target mesh. The body shapes
are automatically estimated from the contours of each human body and can be
retouched if necessary. The brightness diﬀerences between the garment image
and the customer image are adjusted automatically by color correction basedon facial colors. Finally, our system automatically retouches protrusion from therear of the composite garment image.
Our contribution lies in the entire design of a novel virtual fitting systemwith the following features:
1. garment image reshaping based on wearers’ body shapes,
2. automatic color correction using facial colors, and
3. automatic retouching of protrusion behind garments.
We confirm the eﬀectiveness of our system by conducting a user test.

2

Related work

Existing virtual fitting approaches can be divided into two main groups, i.e., 3D
model-based and 2D image-based methods.
There have been proposed 3D virtual try-on systems [4, 15, 5] that can produce compelling fitting results using measured 3D human models and cloth simulation based on physical parameters of garments. Recent dressing simulations [20,3, 10, 22] can reproduce detailed drapes or folds of garments fitted on various different body shapes. Whereas these simulations often use a 3D avatar as a 3D

Image-Based Virtual Try-On System

3

Fig. 2. Overview of our system. Our system first estimates the body-contour models
both for the fashion model and the customer, and then warps the garment cut out
from the fashion model’s image so that the garment matches to the customer’s body
shape. The fitting result is refined further by automatic color correction and automatic
protrusion retouching.

human model, customer’s own 3D model can be generated using depth camera,e.g., Microsoft Kinect, for whole-body scan [11, 19].
Despite these progress, providing an experience as if the customer him- or
herself wore a specific garment is still diﬃcult because photorealistic renderingof 3D human models as well as garments is not handy even now. Additionally,
preparing a huge number of detailed 3D models for garments in online shops iscurrently impractical.
Compared to 3D model-based approaches, 2D image-based methods have advantages that collecting data and photorealistic rendering are relatively easy. As
such, there are example-based approaches that use a pre-recorded database andfind the best matching dataset to provide a virtual fitting result. The system
by Ehara and Saito [8] used a database of marker-attached T-shirt images in
diﬀerent poses. Zhou et al. [24] prepared a database of animating garments in
diﬀerent poses and achieved a real-time virtual fitting by superimposing a garment fitted onto a person captured using Kinect. Hilsmann et al. [12] reproduced
the shapes of garments with detailed folds by interpolating images of garments
worn in diﬀerent poses. These example-based approaches, however, are hardly
applicable in real apparel websites because of the costly database; for example,
for an apparel website [17], photographs of more than ten thousand items aretaken everyday. In contrast, we reduce the load of preparation beforehand, by
inputting only one 2D image for creating data for one garment.

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Y. Kanamori, H. Yamada, M. Hirose, J. Mitani and Y. Fukui

Proposed system

The flow of our system is shown in Figure 2. The inputs to our system are awhole-body image of a fashion model wearing the garment to be used in thefitting (hereafter called a “model image”) and a customer image. We first estimate the body-contour models from contours of the human bodies in the both
input images. The estimated body-contour models can be retouched by user input if necessary. We then determine how the garment is to be reshaped from the
body-contour model of the fashion model and that of the customer. The customer adjusts the position of the garment image to be composited and a virtual
fitting image is output. Finally, a more natural virtual fitting result is obtainedby correcting the brightness of the customer image and retouching protrusions.
3.1

Body-contour modelIn our system, contours of human bodies are used to reshape garments according to the diﬀerences of physique shapes between the fashion model and thecustomer. Ideally, we should know the underlying naked body shapes hidden
by clothes, as done by Guan et al. [9]. Their method trains a parametric 2D
contour model using a database of parametric 3D human models with diﬀerent proportions and diﬀerent poses [1], which requires highly-costly preparation.
Our system instead uses a simple heuristics to estimate a simple 2D shape model
while restricting the input poses in which the arms and feet are slightly spreadto avoid occlusions of body contours (Figure 3). Our 2D shape model, calleda body-contour model, is specific to our system and can be estimated with lesscalculation. Our estimation is of course error-prone, but the user can modify theestimated model easily and quickly, as demonstrated in our user test.
The body-contour model consists of body-feature points and body-featureedges, as shown in Figure 3. The number of body-feature points was determinedthrough experiments regarding estimation easiness and quality of image reshaping. Consequently, we selected a total of 27 points at the neck, shoulder, arm,
wrist, armpits, trunk, hips, leg, and ankle on the left and right sides plus one at
the crotch, as shown by from p0 to p26 in Figure 3.
Heuristic estimation of body-contour model The positions of body-featurepoints are estimated under an assumption that proportional lengths of the bodyparts are common to human bodies even though there are individual diﬀerences.
For example, the position of the arm joint can be predicted as being at a position
about one-third of the length from shoulder to fingertips (Figure 4, left). Our
heuristics is thus to find noticeable points such as necks and shoulders first, andthen apply pre-defined proportions to determine remaining points.
We first extract a person’s contour by tracing the boundary of a pre-definedmask, and sample the contour at M equidistant points (we use M = 200). Of
the points on this contour, we take the uppermost one (with the maximum
y-coordinate) to be the point v0 at the top of the head and number the rest

Image-Based Virtual Try-On System

5

Fig. 3. Body-contour model. It is a 2D shape that consists of 27 body-feature points(from p0 to p26 , red squares) and body-feature edges that connect the points. The blue
circles indicate the reference points used to determine body-feature points.

counterclockwise as vi (i = 0, 1, ..., M −1). We also take the angle subtended by
the vertices vi−1 , vi and vi+1 to be θi ∈ [0, 2π] (counterclockwise is positive).
We select body-feature points from p0 to p26 among the M contour points.
Search for neck and shoulder points. We extract the neck and shoulder points
as the points that give the greatest and smallest values of the angle θi whencounting along a given number of contour points from the top-of-the-head point
v0 . The neck point p0 on the left side is the point at which the angle θi is smallest
within the range of l points (we use l = 10) in sequence counterclockwise fromv0 . Next, the shoulder point p1 on the left side is the point at which the angle θiis greatest within the range of m points (we use m = 10) counterclockwise fromp0 . We search in a similar manner clockwise from v0 for the neck and shoulder
points p26 and p25 on the right side.
Search for fingertips, armpits and others. We then detect noticeable points
such as fingertips and armpits among the contour points by reversals in the ycoordinate, which we use these points as reference when obtaining the bodyfeature points by length ratios, and thus we call them “reference points”. Notethat we do not use the top-of-the-head point as a reference point. The reference

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Y. Kanamori, H. Yamada, M. Hirose, J. Mitani and Y. Fukui

Fig. 4. Ratios used to determine body-feature points. body-feature point P is determined by the ratio of lengths P A and P B along the contour. The left image shows anexample for body-feature point p2 for the right arm. In the left image, the squares arebody-feature points and the circle is a reference point.

points in our system are the following seven points (Figure 3): the fingertips,
armpits, and toetips on the left and right sides, plus the crotch. Regarding
notations, the left and right fingertips are plef t f ingertip and pright f ingertip , theleft and right armpits plef t armpit and pright armpit , the left and right toetips
plef t toe and pright toe , and the crotch pcrotch .
The body-feature points for armpits and crotch are selected from reference
points: p6 = pright armpit , p20 = plef t armpit and p13 = pcrotch . For the remaining body-feature points for the arms and stomach, we determine the positions
according to the pre-defined length ratio between each pair of certain points, as
listed in Figure 4.
Regarding the internal points at the arm and leg parts, we also tried theratio-based approach but often failed because of accumulated errors. We thus
instead find these points from the corresponding external points at opposite sides
according to the contour normals (Figure 5). The pairings of the internal andexternal points are shown in Table 1.
Figure 6 shows a comparison of estimation for body-contour models using
the geodesic shape context [13] and our method. The geodesic shape context
might produce erroneous results as shown in Figure 6(a). Our results shown in
Figure 6(b)(c) have less errors, and the user can manually correct them quickly.
3.2

Garment image reshaping

Using the body-contour models, the garment image is reshaped to fit the customer’s body shape. We used the 2D mesh warping of Weng et al. [21] for reshaping the garment image. Their method suppresses excessive distortions while
trying to keep the area of the input mesh, which seems beneficial for emulating

Image-Based Virtual Try-On System

7

Table 1. Pairings of internal and external body-feature points.
Positions
Arms
WristsLegs
AnklesRight side
Left side
Internal External Internal External
p5
p2
p21
p24
p4
p3
p22
p23
p12
p9
p14
p17
p11
p10
p15
p16

Fig. 5. Comparison of the feature-point calculation. Body-feature point p24 is calculated (a) by length ratio and (b) based on external point p24 . In (b), both p24 and thewrist point are calculated more accurately based on external points.

the real behavior of clothes; e.g., the trunk part of a too-small shirt should become short when worn. To apply their method, we first divide a garment image
into a triangular mesh using constrained Delaunay triangulation. We then automatically set control points for image warping based on body-contour models
as follows.
Automatic setting of control points To apply the method of Weng et al. [21],we select control points and designate their destination positions for reshaping.
Figure 7 illustrates this schematically. Let psn be fashion model’s body-featurepoint and ptn be customer’s. Control points msj are selected for each body-featureedge, and selected among mesh vertices that lie within a certain distance fromeach body-feature edge. Let mtj be the destination position of control point
msj . mtj is calculated from the linear sum of the vectors wn = ptn − psn andwn+1 = ptn+1 − psn+1 as follows:
mtj = msj + (1 − α)wn + αwn+1 ,α=(psn+1 −psn )·(msj −psn )|psn+1 −psn |

2

.

(1)

8

Y. Kanamori, H. Yamada, M. Hirose, J. Mitani and Y. Fukui(a) Geodesic shape context(b) Our result for a male model(c) Our result for a female modelFig. 6. Comparison of body-contour model (red polylines) estimation. (a) Result obtained using geodesic shape context [13] and (b)(c) our results. The failure regions
enclosed by red rectangles in (b)(c) are magnified.

Note that, if the same mesh vertex is selected as control points for multiple
body-feature edges, the vertex is used as the control point only for the nearest
body-feature edge.
3.3

Brightness correction based on facial regions

We perform automatic color correction to suppress the brightness diﬀerences
between the garment image and the customer image. This is based on color
constancy on facial colors, i.e., an assumption that the facial colors of the fashion
model and the customer are the same, and the strangeness is caused solely by
the diﬀerence of the photographic environments. Recently, Bianco et al. [2] also
proposed a method for estimating illumination based on facial colors. Theirmethod learns a database consisting of each pair of a neutral-color referencecard (used to know the ground truth) and a facial color. Our method is much
simpler; we just adjust the brightness only from a pair of images without using
such database.
Our method works as follows. First, facial regions are extracted automaticallyusing Active Shape Model [7] both from the model image and the customer
image. Here we assume that the fashion model is photographed under an ideal
illumination while the customer under an unknown illumination, and the facialcolor of the fashion model fm and that of the customer fc have the following
relationship based on the von Kries model [14]:fm = Dc,m fc ,(2)

where Dc,m is a diagonal matrix that converts colors under the customer’s illumination into those under the ideal (i.e., fashion model’s) illumination. Dc,m isImage-Based Virtual Try-On System

9Fig. 7. Illustration of the calculation of control points from the body-contour model.
The red curve indicates fashion model’s contour while the blue curve the customer’s.
Control point msj is selected as the mesh vertex that is the closest from fashion model’s
feature edge.

then estimated as follows.
Dc,m = diag(fc )−1 fm ,(3)

where diag(x) represents a diagonal matrix consisting of the three componentsof vector x. We then apply matrix Dc,m to the customer image. We confirmedthat the Lab color space yields good results by experiments.
In case that the colors of the fashion model and the customer are largely
diﬀerent, our system lets the customer choose the most appropriate facial color
from face examples with diﬀerent colors.
3.4

Protrusion retouching

Local reshaping of image to reduce protrusions To prevent customer’s
cloth from protruding from the garment image (Figure 8, left), we warp thecustomer image locally (Figure 8, right). For this, we use the method of Schaefer et al. [16] to provide faster feedback. In the following, we describe how toautomatically set control points used for the image warping.
Control point setting Control points are assigned automatically along image
boundaries and customer’s body contour (Figure 11). For image boundaries, 20
control points are set equidistantly for each vertical and horizontal boundary.
For customer’s body contour, 200 control points are set in the same way as thecontour points in Section 3.1.

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Y. Kanamori, H. Yamada, M. Hirose, J. Mitani and Y. Fukui

Fig. 8. Example of protrusion retouching. (a) Before and (b) after the retouching. The
red regions in each image are magnified. The protrusions from the rears of garments
are retouched naturally.

Fig. 9. Open-margin edges (blue) and closed-margin edges (red) of a garment image.
Edges that permit protrusions, e.g., sleeves and neck, are configured as open-marginedges.

Addition of boundary information to garment image Control points
along customer’s contour are not necessarily displaced; some parts of the garment
contour, e.g., the throat and cuﬀs, permit protrusions of customer’s cloth, andthus control points around such parts do not have to be moved. We call suchedges of the garment contour as “open-margin edges”. Conversely, edges aroundshoulders forbid protrusions and we call such edges of the garment contour as“closed-margin edges”. Figure 9 shows an example. By default, all the edges of
the garment contour are set as closed-margin, and we let the user manually markopen-margin edges.
Control points around closed-margin edges are to be displaced, and aresearched within a trapezoidal region formed by extensions in contour normal
directions from both endpoints of each closed-margin edge (Figure 10). If con-

Image-Based Virtual Try-On System

11

Fig. 10. Illustration of control point’s destination. For each closed-margin edge, control
points are searched and moved onto their closed-margin edge.

trol points are found within the search region and are outside of the garment
image, they are registered as to be displaced.
Displacement of control points Figure 10 illustrates how to determine thedestination positions of the control points. For each control point, we first findits belonging closed-margin edge. Let m be the displacement direction vector of
the control point, then m is calculated by linear interpolation of two normals at
both endpoints of the closed-margin edge:m = −(1 − α) N(pn ) − α N(pn+1 )(p −q)×N(p )nn
bef oreα = (pn −qbef ore )×N(pn )+(qbef ore −pn+1 )×N(pn+1 )(4)where N(pn ) denotes the normal at pn and is calculated as an unit vector
orthogonal to the vector pn+1 − pn−1 . × denotes a 2D vector operation, i.e.,a × b = (ax , ay ) × (bx , by ) = ax by − ay bx . qbef ore is the position of the control
point before displacement. Its destination qaf ter is determined by moving qbef ore
onto the closed-margin edge along vector m (Figure 10).

4

Results

Our prototype system was written in C++ using OpenGL and wxWidgets. The
execution environment was a PC with an Intel Core i7-2600 CPU (3.40 Ghz),
and 4 GB main memory, with an NVIDIA GeForce GTX460 GPU.
4.1

Virtual fitting results

Figure 13 shows virtual fitting images using our system. Without garment image
reshaping (Figure 13(c)), the result looks unnatural because the garment image

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Y. Kanamori, H. Yamada, M. Hirose, J. Mitani and Y. Fukui

Fig. 11. Control points (green) and mesh (blue) for the customer image. Control points
are assigned equidistantly along the customer’s contour and the image boundary.

does not conform to the arm and leg positions. With reshaping (Figure 13(d)),
the result becomes better. In Figure 13(e), color correction reduces the brightness diﬀerence caused by the illumination diﬀerences, and the protrusion retouching makes the result look closer to real fitting results. Color correction isespecially useful because customers often take photographs under un-calibrated
lights whereas the fashion model are taken under studio-adjusted lights. The
result in the fifth row demonstrates that our method also works well with aloosely-fitted garment, without introducing unexpected distortion to the garment.
As an application, we can also composite garments onto posed customer
images as shown in Figure 12. Garment images can be warped according toposed body shapes thanks to the underlying meshes. While these results wereobtained by quick manual manipulation of mesh contours, integrating a pose
estimation from a human image as well as skeleton-driven warping would enable
us to automate this process.
4.2

User testing

To evaluate the validity of our results, we conducted experiments with ten students majoring in computer science (six males and four females). We asked
the test subjects to use images of themselves as customer images and evaluate
whether or not the virtual fitting images created by using the customer-sidesystem were natural composite images. We used a five-point Likert scale in theevaluation. We prepared four garment sets as the garment data for the virtual
fitting: (A) coat, (B) T-shirt and jeans, (C) jacket and jeans, and (D) poncho
and skirt. For each of the garment sets, we commissioned the creation of two

Image-Based Virtual Try-On System

13

Fig. 12. Composition of garments onto posed customers. Garment images can bewarped according to posed body shapes thanks to the underlying meshes.

types of virtual fitting image: virtual fitting images obtained by simple superimpositions and images created using our system. The male test subjects were
given the three garment sets (A), (B), and (C) while the female test subjects
were given all of the four sets.
Figure 14 shows the results. The virtual fitting images created using our system were more highly evaluated than those obtained by simple superimposition,
making it clear that natural composite images are produced using our system.
However, there were also comments by test subjects who did not score as highas 5, “the thinning of the clothing conforming to the body shape is unnatural ”
and “the center lines of the body and the garment image are skew.”
Regarding the time for modifying automatically-estimated body-contour models, test subjects took 72 seconds on average. Since this operation need only bedone once for each customer, we believe the time is suﬃciently short.

5Conclusions and future work

We have introduced an image-based virtual fitting system that 1) reshapes thegarment image based on wearers body shapes, 2) automatically adjusts brightness diﬀerences between the garment image and the customer image based on
facial colors, and 3) automatically retouches protrusions of customer’s cloth behind garments. For reshaping, we proposed a simple body-contour model and its
heuristic estimation method. Garment images are warped so that fashion model’s
body-contour model matches customer’s body-contour model. We confirmed thatout virtual fitting results were suﬃciently natural-looking by conducting a user
test.

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Y. Kanamori, H. Yamada, M. Hirose, J. Mitani and Y. Fukui

Future challenges include more advanced virtual try-on by fitting a parametric 3D human model [23] to the customer’s image. That will allow us handle
more realistic 3D eﬀects such as shading changes and cloth deformation.

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Y. Kanamori, H. Yamada, M. Hirose, J. Mitani and Y. Fukui(a) Model images(b) Customer images (c) Naive superimposition(d) Reshaping only(e) Color correction andprotrusion retouching

Fig. 13. Our virtual fitting results. (a) Fashion model images, (b) customer images, (c)naı̈ve superimposition, (d) our virtual fitting results, and (e) results with protrusion
retouching as well as color correction. While naı̈ve superimposition in (c) cannot match
garments to the positions of arms or legs, our system successfully reshapes garments
as shown in (d) and yields more natural fitting results as in (e).

Image-Based Virtual Try-On System

17

Fig. 14. Result of the user test. We asked the subjects whether the virtual fitting
results by our system were satisfactory or not using a five-point Likert scale. The error
bars indicate standard deviations.