# Shapes2Toon: Generating Cartoon Characters from Simple Geometric Shapes

Simanta Deb Turja1  Ahsanullah University of Science
and Technology, Bangladesh
<EMAIL_ADDRESS>Mohammad Imrul Jubair1  University of Colorado
Boulder,
United States
<EMAIL_ADDRESS>Md. Shafiur Rahman  Md. Hasib Al Zadid
Ahsanullah University of Science
and Technology, Bangladesh
<EMAIL_ADDRESS>Ahsanullah University of Science
and Technology, Bangladesh
<EMAIL_ADDRESS>Mohtasim Hossain Shovon 1 these authors contributed
equally. Ahsanullah University of Science
and Technology, Bangladesh
<EMAIL_ADDRESS>Md. Faraz Kabir Khan  Ahsanullah University of
Science
and Technology, Bangladesh
<EMAIL_ADDRESS>
###### Abstract

Cartoons are an important part of our entertainment culture. Though drawing a
cartoon is not for everyone, creating it using an arrangement of basic
geometric primitives that approximates that character is a fairly frequent
technique in art. The key motivation behind this technique is that human
bodies—as well as cartoon figures—can be split down into various basic
geometric primitives. Numerous tutorials are available that demonstrate how to
draw figures using an appropriate arrangement of fundamental shapes, thus
assisting us in creating cartoon characters. This technique is very beneficial
for children in terms of teaching them how to draw cartoons. In this paper, we
develop a tool—shape2toon—that aims to automate this approach by utilizing a
generative adversarial network which combines geometric primitives (i.e.
circles) and generate a cartoon figure (i.e. Mickey Mouse) depending on the
given approximation. For this purpose, we created a dataset of geometrically
represented cartoon characters. We apply an image-to-image translation
technique on our dataset and report the results in this paper. The
experimental results show that our system can generate cartoon characters from
input layout of geometric shapes. In addition, we demonstrate a web-based tool
as a practical implication of our work.

###### Index Terms:

geometric shapes, circles, image-to-image translation, pix2pix, gan, cartoon.

##  I Introduction

In our everyday lives, cartoons are a common creative genre. Apart from
entertainment pursuits, their uses include anything from publishing in print
media to children’s educational narrative [1]. It is not simple for everyone
to create cartoon characters from sketches; rather, we must use a variety of
techniques. The most often used method for a beginner is to begin with some
basic shapes—circles, ovals, triangles, and rectangles—and then build the
character upon them. When we combine these shapes, we get basic designs and
forms of cartoon such as heads, bodies, and buildings [2] [3]. An overview of
the approach is illustrated in Fig. 1. The primary reason behind this method
of using basic geometric shapes is that—things we sketch are fundamentally
composed of basic forms. Looking around, we can see that everything, including
humans, can be broken up into different geometric shapes. By using these
primitives, we can not only produce stylistic proportions but also give our
characters apparent personalities [4]. A competent cartoonist is naturally
adept at extracting the essential elements of an object or person and
representing them with basic shapes [5]. Moreover, this method is often used
in cartoon sketching lessons. The novices—as all artists do—begin with
studying the basic forms and expressions, then go to the little nuances and
quirks that add to the enjoyment of an expression [6]. Therefore, fundamental
geometric forms arguably play a major part in the creation of cartoon
characters.

While the vast majority of individuals are incapable of drawing a cartoon
figure perfectly or in the manner of a professional artist, anyone may make a
structure of the cartoon character using geometric shapes such as circles and
ellipses (as shown in Fig. 1). The approach of building the cartoon character
based on shapes is very effective for children, since the understanding of
geometric shapes is being developed in childhood, essentially based on the
assessment of their responses in the course of tasks associated with
recognition, classification, or explanation of geometric models [7]. Children
can design drawings of characters, as well as numerous articulations of
characters, by just sketching a few shapes [8].

Figure 1: An overview of drawing cartoon using simple geometric shapes. Here
(top row), we first sketch an arrangement of $3$ circles and $2$ ovals (left).
Which can later be useful to build the cartoon faces (middle & right). The
figure is reproduced from [9]. Another example for a different cartoon is
shown in bottom row.

Our paper focuses on this sketching method and attempts to automate it using a
Computer Vision technology. The recent breakthrough of the Generative
Adversarial Network [10] has influenced the researchers to work with image-to-
image translations and to develop different stunning variations, for instance
converting a horse into a zebra [11], a sketch into image [12], a stroke to
motif [13], etc.

### Contribution

Being motivated from the above mentioned works, we aim to create a cartoon
figure utilizing an image-to-image translation method in our work that accept
adjoined geometric shapes from the user and synthesize a cartoon character
depending on the arrangement of those shapes. We call this tool shapes2toon.
We think that our approach will help in revealing the beginner cartoonist’s
visualizing ability and in teaching children to draw. Moreover, it can benefit
a drawing enthusiast and can be used as a sketching tool by anybody, even
those with little drawing ability. Our work has made the following
contributions.

  * •

We propose an approach–shapes2toon–that converts an arrangement of geometric
shapes to a cartoon character. As an application of our approach, we regard
recreating the renowned Disney111https://thewaltdisneycompany.com cartoon
character Mickey Mouse based on the user’s geometric input of shapes. We limit
ourselves to circles and ovals when it comes to basic shapes. For this
purpose, we created a dataset that includes cartoon characters and their
representations in geometric forms. We developed a Javascript-based tool to
manually trace the geometrical approximation of the cartoon characters to
build the dataset. Moreover, we make this dataset available to the community
for future research and can be found here: https://tinyurl.com/shapes2toon-
dataset.

  * •

We presented the results of applying a state-of-the-art image-to-image
translation technique—pix2pix [14]—on our dataset. Additionally, we created a
tool that allows users to draw geometric objects on a digital canvas and our
system would generate the appropriate Mickey Mouse(see fig. 7).

The following describes the structure of this paper. Section II discusses
related studies. In Section III, we describe our dataset followed by an
overview of applying image-to-image translation on it in Section IV along the
outcomes of our experiment. Section V concludes our discussion by outlining
the limits of our research as well as possible future directions.

##  II Background and Related Works

Our work falls under the field of image-to-image (I2I) translation. Hence, we
provide in this section our study on related I2I methods that are pertinent to
our research objective and works as our motivation.

###  II-A Generative Adversarial Network (GAN)

One of the main backbones of I2I is Generative Adversarial Network, or GAN,
which was first proposed by [10] can be considered an unconditional GAN. It
constructs a structured probabilistic model taking latent noise variables $z$
and observed real data $x$ as inputs. GAN is an adversarial method that is
comprised of two neural network models: the generator and the discriminator,
each represented as a differentiable function with parameters. Generator $G$
attempts to create convincing false pictures, while discriminator $D$ is
taught to differentiate between the two. This game’s solution is a Nash
equilibrium between the two participants. The objective optimization problem
is as shown in Eq. 1 [15].

$\displaystyle\mathop{min}\limits_{G}\mathop{max}\limits_{D}{L}(D,G)=E_{x\sim{p_{data}(x)}}[logD(x)]+$
(1) $\displaystyle E_{z\sim{p_{z}(z)}}[log(1-D(G(z)))].$

where $x$ and $z$ denote the real data and random noise vector respectively.
$G(z)$ are the fake samples produced by the generator $G$, and $D(x)$
indicates the probability that $D$’s input is real, and $D(G(z))$ is the
probability that $D$ discriminates between the input from $G$.

A frequently used variant of GAN is conditional GAN (cGAN) [16], which allows
for more control over the output. As a result, the authors suggested
concatenating extra information $y$ with $z$ to create picture $G(z|y)$. The
conditional input $y$ may be any kind of data, including data labels, text,
and picture characteristics.

###  II-B Image-to-Image Translation (I2I)

I2I can be achieved using concept of GAN. In principle, I2I converts an input
picture $x_{P}$ from a source domain $P$ to a target domain $Q$ while
preserving the intrinsic source content and transferring the extrinsic target
style. We need to train a mapping $G_{P\to{Q}}$ that generates image
$x_{PQ}\in{P}$ similar to target image $x_{Q}\in{Q}$ given the input source
image $x_{P}\in{P}$. Mathematically, we can model this translation process as
shown in Eq. 2 [15].

$x_{PQ}\in{P}:x_{PQ}=G_{P\to{Q}}(x_{P}).$ (2)

Authors in [14] demonstrate the application of conditional GAN to the I2I
domain by proposing pix2pix for solving a variety of supervised I2I tasks.
Along with the pixelwise regression loss $L_{1}$ between the translated
picture and the ground truth, this method employs adversarial training loss
$L_{cGAN}$ controlled by hyperparameter $\lambda$ to guarantee that the
outputs are indistinguishable from ”actual” images. The objective is shown in
Eq. 3.

${L}=\mathop{min}\limits_{G}\mathop{max}\limits_{D}{L}_{cGAN}(G,D)+\lambda{L}_{{L}_{1}}(G).$
(3)

In case of pix2pix, paired training samples are used where the input and the
ground-truth image domains are aligned. However, CycleGAN [11] is a very
popular technique for automatically training image-to-image translation models
without the use of paired samples. Here, unsupervised learning is used to
train the models using a collection of pictures from the source and target
domains that are not required to be linked in any way. In the practical field
of computer vision, pix2pix offers a powerful foundation for image
translation, inspiring several enhanced I2I efforts.

####  II-B1 User’s Input Outline based I2I

Numerous improvements and applications have been created based on the
conditional GAN framework. Given that our study is concerned with user input
drawings or outlines, we briefly address several relevant studies.

To start with, SPADE [17]—also known as GauGAN—recommends the use of a
spatially adaptive normalizing layer, for photo-realistic and greater quality
improvement of the synthesized pictures. SPADE utilizes only one style code to
govern the whole style of an image and inserts style information only at the
beginning of a network, rather than throughout. The model allows users to
control the style and content of synthesis results along with generating
multi-modal results.

In SketchGAN [18], the authors proposed a GAN for the completion of sketches.
Their approach can be used to complete input drawings from a variety of
categories of objects. Their main idea is to jointly conduct sketch completion
and recognition tasks.

Another GAN-based end-to-end trainable sketch to image synthesis technique
called SketchyGAN is proposed in [19] which can create objects from $50$
different classes and is GAN-based. This algorithm takes as input an item
drawing and produces as output a realistic picture of the object in a
comparable position to the sketch.

Besides, the authors propose in [20] StrokeGAN for generating Chinese fonts
from unpaired data. A one-bit stroke encoding is used to capture the mode
information of Chinese characters, which is subsequently used to train
CycleGAN [11], reducing mode collapse and increasing character variety. They
train CycleGAN with a stroke-encoding reconstruction loss to maintain stroke
encoding.

Figure 2: The workflow of building our dataset.

####  II-B2 I2I on Cartoon

Since our paper is concerned with the synthesis of cartoon characters, we
provide few existing I2I-based works that focused on the domain of cartoon.

Because many renowned cartoon pictures were developed based on real-world
settings, the authors in [1] were driven to propose CartoonGAN. This is an
unique GAN-based method to picture cartoonization for transforming photos of
real-world scenes into cartoon style images. An unpaired collection of
photographs and a set of cartoon pictures are required for training with this
approach. The reverse task of CartoonGAN is taken into consideration in
toon2real [21], which uses GAN to convert cartoon pictures into photo-
realistic ones. They present a technique for picture translation from the
cartoon domain to the photo-realistic domain that is based on the CycleGAN
model.

The work Auto-painter [22] proposed an auto-painter learning model for
automatically generating painted cartoon pictures from a sketch using cGANs.
Cartoon pictures have more creative color palettes, which may necessitate
additional modeling limitations. The generator is trained using constraints
such as data variability loss, pixel loss, and feature loss in order to
produce more beautiful color collocations. Additionally, the authors provided
a color control for the auto-painter, allowing users to paint in their own
hues.

A number of additional works, such as [23], [24], [25] and [26], that focus on
photo cartoonization have also been produced. The GanToon [27] generates
cartoons which are new in design without any input from the user. The authors
considers the popular cartoon character Tom as their target. There are
additional works that generate Avatar and caricature— for instances [28],
[29], [30], [31] etc.—but are vaguely relevant to our research topic, and thus
we do not go into them in depth in our literature review.

None of the techniques described above is concerned with the generation of
cartoons from a layout of fundamental geometric shapes. In this paper we take
attempt to accomplish this task using pix2pix as our framework. Our primary
challenge in completing this work was the absence of available datasets. As a
result, we create a paired dataset for our pix2pix model, which is discussed
in further detail in the next section.

##  III OUR DATASET

For our shapes2toon, we fix our target character to the famous Mickey Mouse
from Walt Disney. In order to reduce complexity, we only focus on the face of
the cartoon in this paper. Also, to introduce simplicity to the users, we
allow them to input circles and ovals only to approximate the shape of a
Mickey Mouse. Hence, we focus on building a dataset in a pair of two
categories: (1) the combination of circles and ovals that best approximate
Mickey Mouse’s details, and (2) the ground truth of the Micky Mouse character.
Based on this approach, we build our dataset with total of $7500$ augmented
Mickey Mouse images. The steps required for creating the dataset(see fig. 2)
are listed below.

###  III-A Image Collection

Our main source of data collection was YouTube, e.g. Micky Mouse YouTube
channel222https://www.youtube.com/c/MickeyMouse. We first manually selected
the videos that have significance presence of Mickey Mouse, and then extracted
frames from those media files. Since you only want to focus on the Mickey
Mouse, we cropped out the region of the character’s face using image
processing softwares.

###  III-B Pre-processing

Background of each samples needs to be removed for better training of our
model. However, this step is a tedious process as it involves lots of time and
attention to detail—even for experienced designer—via image editing software.
Therefore we exploited AI based background removal tool
remove.bg333https://www.remove.bg/ to serve our purpose. We also processed the
background removed image in order to keep the facial portion only.

###  III-C Approximating Character with Basic Geometric Shapes

As we determined to develop a paired dataset, we required the collected
samples to be processed further to produce the duad. In this step our purpose
was to develop a system that takes the Mickey Mouse character from previous
stage (Section III-B) and generates a layout of circles or ovals that resemble
the character. We initially attempted to construct the approximated layout of
circles and ovals via Hough transformation for ellipse detection [32][33].
However, due to the complex body forms of cartoon characters, hough
transformation failed in the majority of situations. In addition, we also
applied template matching technique [34] with adaptive templates constructed
from interested regions of Mickey Mouse character. However, the results were
quite unsatisfactory in terms of determining where the ROIs were located even
for empirical values of thresholds.

Figure 3: An overview of our tool (left) for approximating a cartoon face with
basic geometric shapes. Examples of augmented paired outputs are shown in the
right.

####  III-C1 Manual Approximation

Earlier mentioned attempts and observations for extracting geometric feature
finally drove us to generate approximated layout of circles and ovals
manually. For this purpose we developed a JavaScript based tool (see Fig. 3)
and assigned volunteers to use then for completing paired dataset. Using this
tool, the volunteers can upload images of Mickey Mouse, navigate through its
different options from its GUI and selects desired geometric primitives (i.e.
circles) to draw it on top of the character. The layouts of the geometric
shapes were stored as a seperate image files. The shapes can be adjusted—such
as rotated, scaled, shifted, etc—based on the contents of the uploaded
cartoon.

####  III-C2 Data Augmentation

Using our tool, We created $500$ cartoon images with their geometrical
representation. An additional feature of this tool is the integrated image
data augmentation [35]. We apply different versions for each paired
samples—such as rotated, scaled, flipped, translated—using the feature of our
tool to obtain results from the model (see Fig. 3). The final augmented
dataset contains $7500$ images of cartoon characters.

Figure 4: Training of Pix2pix to associate shapes with cartoons. The
discriminator learns to distinguish between fake (generator-generated) and
real {shapes, cartoon} tuples. The generator learns to fool the discriminator.
Figure 5: Results of our shapes2toon for training on original dataset (without
augmentation). Here, left column shows input geometric layout, middle column
shows the generated output based on input. Ground truths are shown in right
column.

##  IV Implementation of pix2pix and Experiments

To learn a mapping from geometric shapes to actual Mickey Mouse, we used the
pix2pix model to train our dataset. The concept of pix2pix is already
described in Section II. Fig. 4 shows the architecture of our pix2pix
implementation. Here, the generator is a U-Net based encoder-decoder model
[36]. The model begins with a source picture (for example, our layout of
geometric shapes) and produces a target image (e.g. cartoon image). This is
accomplished by downsampling or encoding the input picture to a bottleneck
layer and then upsampling or decoding the bottleneck representation to the
output image’s size. The U-Net design entails the addition of skip-connections
between the encoding and decoding layers, producing a $U$-shape.

Unlike the standard GAN model, which classifies pictures using a deep
convolutional neural network, the Pix2Pix model employs a PatchGAN
discriminator. This is a deep convolutional neural network that was developed
to identify individual $N\times N$ patches of an input picture as real or
false, rather than the full image. We used a patch size of $70\times 70$ which
is usually effective across a range of image-to-image translation tasks [37].

Figure 6: Results of our shapes2toon for training on augmented dataset
(without augmentation). Here, left column shows input geometric layout, middle
column shows the generated output based on input. Ground truths are shown in
right column. Figure 7: A snap of our shapes2toon web application. Here, user
can draw shapes’layout via draw button and generate the corresponding Mickey
Mouse face via transfer. User can clear the campus any time. For simplicity, a
predefined layout can be uploaded using select option. User also can see a
sample output from our existing collection of input by clicking Random button.

###  IV-A Experimental Setup and Results

(a)

(b)

Figure 8: Shows generator, discriminator and $L1$ loss. For original dataset:
(a) Training process started with a generator loss of $0.395$, discriminator
loss of $0.874$ and the $L_{1}$ loss of $19.831$ and ended with $2.027$,
$0.462$, $26.418$ respectively. For our augmented dataset: (b) Training
process started with a generator loss of $3.437$, discriminator loss of
$-0.238$, $L_{1}$ loss of $19.97$ and ended with $7.069$, $-0.124$, $31.46$
respectively.

We evaluated our method by conducting experiments on our main dataset as well
as the augmented dataset. The main dataset has $500$ images, whereas the
augmented dataset contains $7500$ images. The images are scaled to $512\times
256$ pixels, with adjoined geometric forms and the original cartoon side-by-
side, each of which is $256\times 256$ pixels in size. Both datasets were
split into $95.8$ percent training and $4.2$ percent testing ratios.

For all datasets, we set the number of filters for the both generator and
discriminator, $Ng=64,Nd=64$. We started training the model with a learning
rate of $0.0002$ having a batch size of $1$ and, trained the network for $30$
epochs and $50$ epochs for the main dataset and the augmented dataset
respectively. The training and testing process carried out on Tesla K80 and
cuda V10.1 embedded in Google Colaboratory.

We perform quantitative and qualitative studies to validate our method. Fig. 5
and 6 illustrate several qualitative visualizations. For quantitative
assessment, we use Frechet inception distance (FID) [38] in this paper. The
reason behind choosing Frechet inception distance, the distribution of
generated images is compared to the distribution of a set of real images by
FID unlike the earlier inception score which evaluates the distribution of
generated images without any ground truth. Lower FID values indicate closer
distances between synthetic and real data distributions. The Pix2pix model
achieved an FID score of $244.651$ on the main dataset and an FID score of
$183.403$ on the augmented dataset. Fig. 8 depicts the training loss graphs on
our both datasets.

(a)

(b)

Figure 9: Our attempts to generate full body of Mickey Mouse character. Here
(a) shows an example of creating a paired sample for the dataset while
considering the full body. Some results of applying pix2pix on full-body
dataset are shown in (b).

##  V Conclusion and Future Works

In this research, we presented a technique called shapes2toon that tries to
automate this process by employing a generative adversarial network that mixes
geometric primitives (such as circles and ovals) to produce a cartoon
character (such as Mickey Mouse) based on the supplied approximation. We were
inspired from the fact that drawing basic geometric primitives as an initial
approximation of a cartoon is commonly used by people. We generated a
collection of geometrically represented cartoon characters from YouTube source
to build a paired dataset for this purpose. On our dataset, we used an image-
to-image translation approach (pix2pix) and described the findings in this
paper. The results of the experiments indicated that our system can produce
cartoon characters from a geometric form input arrangement. In addition, as a
practical application of our research, we presented a web-based utility. I
reckon, our technique can be a helpful option for children and novices to
learn cartoons. However, there are some limitations of our work such as
dealing with the full body of the cartoon. We already attempted to expand our
dataset for including the entire body; however due to the extreme nature of
the cartoon bodies and diversity, the results were not satisfactory (see Fig.
9). Moreover, we currently focus on a specific cartoon character and highlight
on the facial region only.

Future Works. The analysis on the present limitation opens the future avenue
to improve our work. Based on the discussed issue we list the following tasks
as our future plans.

  * •

We plan to expand our dataset by collecting more images of various cartoon
character that will be suitable for full body of the cartoon character. We
need a robust cartoon figure extraction tool for this purpose.

  * •

In future, we want to experiment on different variants of pix2pix and provide
necessary ablation studies. We also desire to build a dedicated architecture
for our shapes2toon.

  * •

Applying different quality assessment is also one of our future targets. We
plan to implement AMT perceptual studies which are used in many image to image
translation methods, such as [39] [40].

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