Finding Rules of Attractive Human Poses Using Decision Treeand Generating Novel Attractive Poses
Masaki Oshita

Kyushu Institute of Technology
680-4 Kawazu
Iizuka, Fukuoka, 820-8502, Japanoshita@ces.kyutech.ac.jp

Kei Yamamura

Kyushu Institute of Technology
680-4 Kawazu
Iizuka, Fukuoka, 820-8502, Japan
n236074k@mail.kyutech.jp

ABSTRACTIn this paper, we propose a method to determine the rules
for attractive poses and generate novel attractive poses basedon the discovered rules. Given a set of attractive poses witha speciﬁc style and another set of unattractive poses, we
can obtain the rules to separate those two sets of example poses by creating a decision tree based on the low-level
pose features that are computed from the example poses. In
this research, we implemented our approach for two kinds ofattractive poses, Hero and JoJo standing poses, and successfully discovered the rules of these styles. We also developed aheuristic kinematics-based pose deformation method basedon the discovered rules of attractive poses. The rules can beapplied to any input pose with any speciﬁed scale. Using oursystem, a creator can interactively design novel poses.

CCS CONCEPTS• Applied computing → Media arts; • Computing
methodologies → Perception; Shape analysis;

KEYWORDS
Human Pose, Attractiveness, Decision Tree
ACM Reference format:
Masaki Oshita, Kei Yamamura, and Aoi Honda. 2017. Finding
Rules of Attractive Human Poses Using Decision Tree and Generating Novel Attractive Poses. In Proceedings of CGI ’17, Yokohama, Japan, June 27-30, 2017, 6 pages.
DOI: 10.1145/3095140.3095173

1

INTRODUCTION

Attractive poses of human characters often appear in various
types of media content such as movies, animations, computer
games, illustrations, comics, and action ﬁgures. The creators
must design attractive poses that fall within a certain styleand are novel and eye-catching. Because there are no speciﬁc
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DOI: 10.1145/3095140.3095173

Aoi Honda

Kyushu Institute of Technology
680-4 Kawazu
Iizuka, Fukuoka, 820-8502, Japan
aoi@ces.kyutech.ac.jp

deﬁnitions or rules of attractive poses, the creators mustdesign them based on their experience and through trial anderror. This is a very diﬃcult and time-consuming task.
The purpose of this research is to establish a method todetermine the rules of attractive poses and to generate novel
attractive poses based on the discovered rules. Because there
are diﬀerent types of attractive poses with diﬀerent styles,
and there are no universal rules applicable to all kinds ofattractive poses, we determine the rules of attractive poses
for each style using an example-based approach. Given a
set of attractive poses with a speciﬁc style and another setof unattractive poses, we can obtain the rules to separate
the two sets of example poses by creating a decision tree
[8] based on the low-level pose features that are computedfrom the example poses. Of the many existing classiﬁcation
methods, we choose to use a decision tree because it can represent the rules for classiﬁcation in a way that we can understand, which is important for using the discovered rules
to create new attractive poses. In this research, we implemented our approach for two kinds of attractive poses, Hero[13] and JoJo [1] standing poses, and successfully discovered the rules of these styles. We also developed a heuristic kinematics-based pose deformation method based on the
discovered rules. The rules can be applied to any input posewith any speciﬁed scale. Using our system, a creator caninteractively design novel attractive poses.

2

RELATED WORK

Attractiveness is an important factor in content creation.
However, it is a diﬃcult factor to evaluate. Although there
are some subjective evaluations for human motions [6], thespeciﬁc rules of attractiveness are still unclear. Many methods have been proposed for pose or motion categorization[15]. They mostly consider the spatial distances between
poses. Subjective factors such as attractiveness are not taken
into account. We classify attractive and unattractive posesets using low-level pose features and a decision tree. Somehigh-level pose features have been introduced in previous
studies [4]. However, such high-level features are only applicable for a speciﬁc range of poses. To handle a wide rangeof poses, many pose features would be required.
For pose deformation, we implemented a kinematics-based
method. Although similar approaches have been used before[10, 11], our method focuses on the pose features that appear
in the discovered rules of attractive poses.

CGI ’17, June 27-30, 2017, Yokohama, Japan(a) skeleton model(b) skinned model

M. Oshita et al.

(c) input device

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Figure 1: Pose representation and creation.

3

ATTRACTIVE POSES DEFINITION

As explained above, we deﬁne each type of attractive poseusing examples. Our research focuses on the static standing
poses of a male character with an average physique who does
not hold any props. We also assume that the attractivenessof a pose is view independent. In some media content suchas illustrations and comics, the viewing direction of the posecan be important. However, in general, the pose can be seenfrom any direction on other media content such animation,
computer games, and action ﬁgures. Therefore, we focus on
the view-independent attractiveness of poses. Although it ispossible to extend our method to considers other factors suchas body shape (including gender and age), viewing direction,
context, and background, these parameters were not taken
into account in this study so that the attractiveness of poses
could be evaluated independently. In addition, we assume
that the attractiveness of a pose does not change when thepose is mirrored (i.e., ﬂipped horizontally). This becomes
important when we deﬁne pose features.

3.1

Pose Representation

Given a human skeleton model, as shown in Figure 1(a), a
pose is represented by the position and rotation of the pelvis
and the rotations of all joints. We used a skeleton model with
16 joints and 15 segments. A skinned human model is also
used to display poses, as shown in Figure 1(b). Although
we could use a stick model like the one in in Figure 1(a) todisplay the poses, it is diﬃcult to observe pose details suchas the orientation of the head. Therefore, we use the skinned
human model of an average male for the visual presentation.
Because the skinned model is not used for computing thepose features, the choice of skinned model does not becomea problem when creating the rules for attractive poses.

3.2

Pose Creation

We developed a pose editing system for creating example
poses. We used a QUMARION pose input device [3] (Figure1(c)), which is a physical action ﬁgure with sensors to measures joint rotation so that poses can be easily created, evenby an ordinary user who is not trained to use professional authoring systems. We also implemented a mouse-based pose
editing interface so that the created poses can be adjusted
when necessary.

Figure 2: Examples of attractive Hero poses.

3.3

Example Pose Sets

In this research, we implemented our approach for two stylesof attractive poses: Hero and JoJo styles. We chose these twostyles because they are well-known popular styles in Japanese culture and materials such as picture and illustration
books for these styles are available.
We created 30 example attractive poses for each style
based on example poses that are taken from the picture andillustration books. In addition, we also created 30 examples
of unattractive poses.
Hero poses (Figure 2) are the ﬁrst type of attractive poseused in this study. Suited action heroes in Japanese movies
often appear in Hero poses. When a hero or heroine faces
the villain during a ﬁght scene, they often make strong anddeﬁant poses. The example poses are chosen from a picturebook [13] that is meant to provide examples for creators.
JoJo poses (Figure 3) are the other type of attractive
pose considered in this study. The JoJo standing poses appear in the comic series JoJo’s Bizarre Adventure by Hirohiko Araki [1], which is very famous and popular in Japaneseculture. The JoJo standing poses have a very unique style.
They are often imitated in many other media as well. The
example poses were chosen from an illustration book [2].
Unattractive poses (Figure 4) are ordinary standing
poses. They were chosen from various books [9, 14]. Of themany possible example poses, we selected ordinary posesthat are dissimilar to the Hero and JoJo poses.

Finding Rules of Attractive Human Poses Using Decision Tree

CGI ’17, June 27-30, 2017, Yokohama, Japan

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Figure 3: Examples of attractive JoJo poses.

4

FINDING THE RULES OFATTRACTIVE POSES

Given a set of attractive poses with a speciﬁc style and another set of unattractive poses, we determined the rules toseparate the two sets of poses by creating a decision tree.

4.1

Poses Features

We compute a large number of low-level pose features foreach example pose (381 in total). Of them, a decision tree
automatically chooses a few important features that are effective for separating attractive and unattractive poses foreach style.
All the pose features are represented by a one-dimensional
scalar value (signed or unsigned). Three-dimensional positions and rotations are divided into a combination of single
variables. As mentioned in Section 3, the attractiveness ofa pose should not change when the pose is mirrored (i.e.,
ﬂipped horizontally). Therefore, instead of using pose features from the right and left sides of the pose, we used theaverage and absolute diﬀerence between the values from bothsides. For example, we use the average and absolute diﬀerence between the joint rotations of the right and left elbows.
The pose features are categorized as follows.
Local joint rotations are computed directly from thepose representation (Section 3.1). Each joint rotation is divided into yaw, pitch, and roll rotational angles. These angles
are used as pose features. The joint rotations on the center

Figure 4: Examples of unattractive poses.

joints such as the trunk, chest, neck, and head are directly
used. The joint rotations on the right and left sides are indirectly used by computing the average and absolute diﬀerence
between the rotation angles of both sides.
Global segment orientations are computed from theorientation of each segment with respect to the ground. They
are computed based on the pose and skeleton model using
forward kinematics. These rotations are also divided into
yaw, pitch, and roll rotational angles. However, because eachpose can take any horizontal orientation and the horizontal
orientations (yaw angles) of the segments are irrelevant, they
are discarded. Only the pitch (bending) and roll (tilting)angles are used as pose features.
Body segment heights are also computed from the poseand skeleton model using forward kinematics. Because thehorizontal positions are also irrelevant, only the height fromthe three-dimensional positions of each segment is used as apose feature.
The center of mass height is also computed from thepose and skeleton using the body weights, which are estimated from the skinned human model.
Distances between a pair of body segments are computed from the global positions of the body segments. We
use the three-dimensional, vertical, and horizontal distancesfor each pair of body segments. Again, the average and absolute diﬀerence are computed for body segments on the rightand left sides of the pose.

CGI ’17, June 27-30, 2017, Yokohama, Japan

M. Oshita et al.

Good : 60
Bad : 60
bending angle ofhead w.r.t neck> - 0.045 and <= 0.15

> 0.15 and <=0.45

Good : 36
Bad : 0<=-- 0.045 or > 0.45

Good : 22
Bad : 28

Good : 2
Bad : 34

absolute orientation angle ofhead w.r.t trunk<= 0.11> 0.11

Good : 16
Bad : 4

Good : 6
Bad : 22absolute orientation angle ofhead w.r.t waist<= 0.77
Good : 14
Bad : 0> 0.77
Good : 2
Bad : 4

Figure 5: Decision tree for Hero poses.

4.2

Decision Tree

Decision trees are a non-parametric supervised learning
method used for classiﬁcation. The results are represented as
simple trees, which are visible and easy interpreted. We usedthe decision trees implemented in IBM’s SPSS [7], which is
a commercial software for statistical data analysis. It oﬀers
several methods for creating tree models. We selected thedefault method, chi-squared automatic interaction detection[8], which uses chi-square statistics to identify optimal splits.
This method ﬁrst examines the cross tabulations between
each of the input ﬁelds and the outcome, and tests for signiﬁcance using a chi-square independence test. If more than
one of these relations is statistically signiﬁcant, it will selectthe input ﬁeld that is the most signiﬁcant, that is, the one
with the smallest p-value. Continuous input data is divided
into equal intervals and treated as ordered categorical data.

4.3

Discovered Rules of Attractive Poses

By changing a parameter that determines the processing
condition, diﬀerent trees with similar accuracies can be generated. We chose a decision tree that contains fewer posefeatures and conditions and provides intuitive rules.
The generated decision trees are presented and explained
below. In Figures 5 and 6, each node contains a condition
to select one of the lower nodes based on a pose feature. At
each node, “good” and “bad” represent the numbers of attractive and unattractive poses, respectively. The conditions
for selecting the nodes that contain many good poses are
extracted as the rules for attractive poses.
4.3.1 Hero Poses. Figure 5 shows the decision tree generated for Hero poses. The recognition accuracy for all goodand bad poses is 95.0%. In this case, almost all of the good
poses can be categorized into two similar clusters. Three conditions are important for describing the two nodes (the graynodes in Figure 5) that contain most of the good poses.

Hero pose: The bending angle of head with respect tothe neck should be between −0.045 and 0.15. If the bending angle is between 0.15 and 0.045, the absolute value ofthe horizontal orientation angles of the head with respect tothe trunk and waist should be less than 0.11 and 0.77, respectively. In short, when both the bending and orientation
angles of the head are small (i.e., the head is facing forward),
the pose is considered to be a good pose.
It is surprising that the hero poses can be distinguishedjust by the head orientation. However, as we can observe in
Figure 2, most of them indeed contain this characteristic.
4.3.2 JoJo Poses. Figure 6 shows the generated decision
tree for JoJo poses. The recognition accuracy for all goodand bad poses is 91.7%. The good poses are categorized into
four clusters with diﬀerent conditions. The conditions forgood poses vary depending on the ﬁrst condition at the topof the tree, which is the bending angle of the trunk.
JoJo pose A: When the bending angle of the trunk withrespect to the ground is less than −0.063 (i.e., bent backward), the bending angle of the head with respect to thepelvis should be should be less than 0.22. In short, when
the trunk is bent backward, the bending angle of the headshould be straight. Typical poses in this category are poses
1 and 24 in Figure 3.
JoJo pose B : When the bending angle of the trunk
with respect to the ground is between −0.063 and 0.46 (i.e.,
straight), the absolute value of the diﬀerence between thebending angles of the left and right knees should be larger
than 0.59. In short, when the trunk is straight, the bending
angles of the knees should be asymmetrical. Typical posesin this category are poses 9 and 16 in Figure 3.
JoJo pose C : When the bending angle of the trunk
with respect to the ground is between −0.063 and 0.46 (i.e.,
straight) and the absolute value of the diﬀerence between
the bending angles of the left and right knees is smaller than
0.59, the absolute value of the horizontal orientation of theneck with respect to the trunk should be between 0.0078 and0.01. In short, when the trunk is straight, the head should
be facing forward. Typical poses in this category are poses
11 and 26 in Figure 3.
JoJo pose D : When the bending angle of the trunk withrespect to the ground is larger than 0.46 (i.e., bent forward),
the absolute value of the diﬀerence between the bending angles of the left and right elbows should be larger than 0.15.
In short, when the trunk is bent backward, the bending angles of the elbows should be asymmetrical. Typical poses inthis category are poses 12 and 29 in Figure 3.
Although the discovered rules are a little complex compared to the Hero poses, they are understandable and reasonable. As Figure 3 shows, most of the JoJo poses have distinct characteristics in the bending angle of the trunk andin the asymmetric poses of the arms and legs.
4.3.3 Summary. We conclude that the rules discovered
using the generated decision trees are understandable andreasonable. These rules should be useful when a creator ismaking attractive poses in these categories.

Finding Rules of Attractive Human Poses Using Decision Tree

CGI ’17, June 27-30, 2017, Yokohama, Japan
Good : 60
Bad : 60

bending angle of trunk w.r.t the ground> - 0.063 and <= 0.46
Good : 24
Bad : 48<= - 0.063
Good : 16
Bad : 18
bending angle ofhead w.r.t. waist<= 0.22> 0.46
Good : 20
Bad : 4absolute difference of bending
angles of left and right elbows

absolute difference of bending
angles of left and right knees<= 0.59< 0.22

Good : 12
Bad : 0Good : 4
Bad : 8A

> 0.59Good : 10
Bad : 46

Good : 4
Bad : 46

Good : 2
Bad : 4

> 0.15
Good : 18
Bad : 0B

orientation angle ofneck w.r.t. trunk<= 0.0078 or> 0.010<= 0.15

Good : 14
Bad : 2D

> 0.0078 and<= 0.010
Good : 6
Bad : 0C

Figure 6: Decision tree for JoJo poses.

5

POSE GENERATION

We developed a pose deformation method based on the discovered rules of attractive poses. The rules can be applied
to any input pose to any speciﬁed scale. We deﬁned posedeformation parameters based on the pose features that areused in the rules. Using these parameters, an input pose canbe deformed using a heuristic kinematics-based method.
We implemented a pose design system. Based on the discovered rules of attractive poses, the system can determine
whether an input pose satisﬁes the rules. An input pose canbe selected from a set of example poses or created using
the pose input device and our editing interface, which is described in Section 3.2. The user can also select which styleand category is applied. In addition, the user can adjust thescale of attractiveness so that the input pose is deformed
using the selected rules. The pose is increasingly deformed
when the user increases the scale. Using our system, the creator can interactively deform any input pose based on therules and hence design novel attractive poses.

5.1

Adjusting Pose Features

The target values of the pose features are determined basedon the selected style and category and the speciﬁed scale of
attractiveness. When the scale is between 0.0 and 0.5, it islinearly interpolated from the initial value, which is computefrom the initial pose to the threshold using the discovered
rules. When the scale is between 0.5 and 1.0, it is linearlyinterpolated from the threshold to an extreme value that is
manually speciﬁed in advance. If the rules deﬁne a feature
range, the center of the range is used as the extreme value.

5.2

Pose Deformation Parameters

We deﬁned the parameters shown in Table 1 to deform an
input pose while satisfying the conditions in the rules. Because the pose features cannot be directly controlled, they
are adjusted through the pose deformation parameters. The

Table 1: Pose features and the corresponding posedeformation parameters.
pose features
the bending angle ofthe trunkthe rotation of the headthe diﬀerence between
the bending angles of
the right and left elbows
the diﬀerence between
the bending angles of
the right and left knees
additional balance control

pose deformation parameters
the orientation of the pelvis,
the rotation of the backthe rotations of
the neck and head
the rotations of the right andleft elbows
the lateral position of
the pelvis
the horizontal position of
the pelvis

target deformation parameters are computed from the target
pose features.

5.3

Pose Deformation

An input pose is deformed based on the target deformation
parameters. As shown in Table 1, we also apply an additionalbalance control so that the ﬁgure maintains its balance.
First, the upper body pose, including the pelvis orientation, is computed. The rotations of the pelvis, back, neck,head, left elbow, and right elbow are applied to the pose.
Second, the lower body pose is computed to maintainthe balance of the ﬁgure. The lower body pose is controlled
through the position of the pelvis. All the joint rotations on
both legs are computed using inverse kinematics [12] so thatthe positions of both feet are kept. The horizontal position(two-dimensional position) of the pelvis is computed to keep
the balance so that the horizontal position of the center ofmass remains in the same place after the upper body posedeformation.

CGI ’17, June 27-30, 2017, Yokohama, Japan

input
output(a) application of Hero pose rules

M. Oshita et al.

input
output(b) application of JoJo pose B rules

input
output(c) application of JoJo pose D rules

Figure 7: Results of pose deformation. The rules of the selected type and category are applied to the input.
Third, the lateral position (one-dimensional position) of
the pelvis is further adjusted. It is not possible to simply
change the bending angles of the knees, because such a posedeformation breaks the contact constraints between the feetand the ground. Therefore, we control the diﬀerence between
the bending angles of both knees by moving the pelvis in thelateral axis. For example, if the pelvis is moved over the rightfoot, the right knee is bent and the left knee is extended. Thisway, the diﬀerence between the bending angles of both knees
can be controlled. The lateral axis is determined based on thepositions of both feet. The target lateral position is linearlyinterpolated between the initial value and foot position. Notethat the required pose feature may not be satisﬁed dependingon the input pose. For example, when both feet are close toeach other, there is not much room to control the lateralpelvis position without moving the foot positions. This is alimitation of our method. However, it works well when thefeet are far enough apart.

6

RESULTS AND DISCUSSION

Some results of the pose deformation are presented in Figure
7. Although we have not done a formal evaluation, as theattractiveness of poses is subjective and not easy to measure,
it appears that our method has worked as expected.
Because we employ a simple kinematics-based pose deformation, the resulting pose may become unnatural depending on the input pose, especially when a large deformation
is applied. Because we provide an interactive pose deformation interface, the user can adjust the scale of deformation
so that the pose does not become unnatural. We could takean example-based approach [5] and limit the pose deformation within a set of given example poses. However, because
our purpose is to produce novel poses that are not includedin the example poses, the example-based approach does notseem to ﬁt well. Because our current method is based on thepose features for the Hero and JoJo poses, other deformation parameters and methods would be required to deal withother styles. This remains as future work.

7

CONCLUSION

In this paper, we proposed a method to determine the rulesof attractive poses and generate novel attractive poses basedon the discovered rules. We implemented our approach for

two kinds of attractive poses. We successfully discovered therules of these two styles and achieved pose deformation. Although we still need to experiment on diﬀerent types of attractive poses and extend the pose deformation method, we
believe that our approach is promising and can be an eﬀective way to create novel attractive poses.

ACKNOWLEDGMENT
This work was supported in part by Grant-in-Aid for Scientiﬁc Research (No. 15H02704 and No. 15K05003) from theJapan Society for the Promotion of Science (JSPS).

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