Generation of Emotional Dance Motion for Virtual Dance Collaboration System

poster / demo / art installation
  1. 1. Tsuruta Seiya

    School of Science and Engineering - Ritsumeikan University

  2. 2. Woong Choi

    Global Innovation Research Organization - Ritsumeikan University

  3. 3. Kozaburo Hachimura

    College of Information Science and Engineering - Ritsumeikan University

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Generation of Emotional
Dance Motion for Virtual
Dance Collaboration
Seiya, Tsuruta
Graduate School of Science and Engineering,
Ritsumeikan University, Japan
Woong, Choi
Global Innovation Research Organization,
Ritsumeikan University, Shiga, Japan
Kozaburo, Hachimura
Department of Media Technology, College
of Information Science and Engineering,
Ritsumeikan University, Shiga, Japan
Measurement of body motion using motion
capture systems has become widespread in
the fields of entertainment, medical care, and
biomechanics research.
In our laboratory, we are undertaking research
on the application of digital archiving and
information technology to dancing [Hachimura,
2006]. For example, quantitative analysis
of traditional dance motion [Yoshimura et
al., 2006] and 3D character animations of
traditional performing arts using virtual reality
[Furukawa et al., 2006]. Recently, we have
measured many kinds of dance motion, not
only Japanese traditional dances, but also
contemporary street dances.
Dance collaboration system is one of the typical
collaboration systems based on body motion.
In our laboratory, we are developing a Virtual
Dance Collaboration System [Tsuruta et al.,
2007]. Live dancer’s motion is captured by
optical motion capture system, and the dance
collaboration system recognizes it in real-time.
A virtual dancer responds to the live dancer’s
motion. The live dancer performs a dance to the
music, and a virtual dancer reacts with a dance
by using the motion data stored in a motion
database. It is desirable to generate a virtual
dancer’s motion according to the live dancer’s
emotion or music emotion.
In this paper, we describe a method to
generate emotional dance motions by modifying
a standard dance motion which is stored in a
1. Overview of the Virtual Dance
Collaboration System
A configuration of the system is shown in
Figure 1. Our proposed system provides users
with collaboration with virtual dancers through
dancing. The collaboration system consists
of an optical real-time motion capture and
an immersive virtual environment system. An
optical motion capture system is able to
measure body motion precisely and in real-time.
Immersive virtual environment provides users
stereoscopic display and feeling of immersion.
The system has three sections: “Motion
processing section”, “Music processing section”
and “Graphics processing section”.
In the “Motion processing section”, the system
recognizes a live dancer’s motion in real-
time, and determines a virtual dancer’s reactive
motion. In the “Music processing section”,
the system extracts emotional information
from music in real time. In the “Graphics
processing section”, the system regenerates
a virtual dancer’s motion by using extracted
emotional information from music, and the
system displays 3DCG character animation by
using immersive virtual environment.
Fig. 1. Configuration of Virtual Dance Collaboration System
2. Generation of Emotional Dance
For our system, it is necessary to generate
a virtual dancer’s emotional motion in real-
time. We developed a system named Emotional
Motion Editor (EME).

Table 1 - Motion features appeared on each human emotion
Table II - Parameters used for modifying motion data
: coefficient
2.1. Emotional Motion Editor
The EME generates emotional dance motions by
modifying the original motion data by changing
the speed of motion or altering the joint angles
interactively. To generate an emotional motion,
a function of changing the size of motion is
implemented within the EME.
To generate virtual dancer’s emotional motions
in real-time, we need a simple method which
can calculate with few order. For this purpose,
we employ a method altering the interior angle
of two connecting body segments as shown in
Figure 2.
Fig. 2. Altering the joint angle
is an original vector and a vector
after rotating respectively. Where
is an
original angle, and
is an after rotating angle
respectively. The rotation matrix is calculated by
using equation (1).
− θ
) matrix for rotation about
is a normal vector represented by
equation (2). Where × means outer product.
Change the size of the motion is indicated by
equation (3).
Fig. 3. Screen shot of Emotional Motion Editor
are coefficient for
amplification and bias respectively. Where
amplified angle,
is average of angles in sliding
window at frame
as shown in equation (4).
a half of the size of the sliding window.
A screen shot of the EME is displayed in
Figure 3. A character model on the left shows
an original dance motion. A model on the
right shows generated emotional motion after

2.2. Relation between Emotion and
Body Motion
We examine the correlation between emotion
and body motion in dancing by interviewing
the dancer. We employ 5 kinds of emotions
(Neutral, Passionate, Cheerful, Calm, Dark).
“Neutral” is a standard motion. Motion features
appeared on each human emotional motion are
shown in Table I. We then obtained parameters
empirically with a dancer. Parameters used
for generating emotional motions are shown
in Table II. Figure 4(a) shows an example of
motion modification. A thin line in Figure 4(b)
shows an original graph of angle variation of
the right knee. The thick line shows a modified
graph. In this case,
was given 3.0 and
-25.0 respectively.
3. Experiments
We generate 4 kinds of emotional motions by
using EME according to Table II.
To evaluate generated emotional motions, we
conducted 2 types of assessment experiment by
using questionnaire survey.
3.1. Method of Experiment
Experiment 1
Experiment 1 is a comparison between neutral
standard motion and artificially generated
emotional motions.
Experiment 2
Experiment 2 is a comparison between motion-
captured emotional motions and artificially
generated emotional motions.
For the experiment, we used 9 kinds of motions
as the following:
4 Emotional motions (performed by dancer)
1 Standard motion (performed by dancer)
4 Artificial emotional motions (generated by
3.2. Result of Experiments
The results of Experiment 1 are shown in Figure
5. This figure shows score averages, standard
deviations and significant differences by the
t-test. Black circles show standard motions,
triangles show generated artificial emotional
motions. As shown in Figure 5, all kinds of
scores except "Calm" are higher than standard
motion. We found that the respondents receive
an impression of each emotion through artificial
emotional motion.
Figure 6 indicates the results of Experiment 2.
White circles show motion-captured emotional
motions, and triangles show generated artificial
emotional motions by using EME. As a
result of the t-test, there is no significant
difference. We found that respondents received
similar impressions of emotional motions from
generated artificial emotional motions. We
verified that our EME system is effective in
generating emotional motions.
Fig. 5. Experimental result 1
Fig. 6. Experiment result 2

4. Conclusion and future works
In this paper, we described a method to generate
emotional dance motions by modifying the
standard dance motion.
To generate emotional motions, we developed
the Emotional Motion Editor. We conducted two
experiments to evaluate generated emotional
motions. As a result, we confirmed that EME can
generate emotional motions by altering motion
speed and joint angles.
As future work, implementation of the motion
processing section and the motion modification
function is necessary in the Virtual Dance
Collaboration System.
This research has been partially supported by
the Global COE Program “Digital Humanities
Center for Japanese Arts and Cultures”,
the Grant-in-Aid for Scientific Research No.
(B)16300035, all from the Ministry of
Education, Science, Sports and Culture. We
would like to give heartfelt thanks to Prof.
Y. Endo, Ritsumeikan Univ. whose comments
and suggestions were of inestimable value for
our research. We would also like to thank Ms.
Gotan and Mr. Morioka who support many
Furukawa, al.
(2006). CG Restoration
of Historical Noh Stage and its use for
Proc. VSMM06.
Pp. 358-367.
Hachimura, K.
(2006). 'Digital Archiving
of Dancing'.
Review of the National
Center for Digitization (Online Journal).
(accessed 12 March 2010).
Tsuruta, S. et al.
(2007). 'Real-Time
Recognition of Body Motion for Virtual Dance
Collaboration System'.
Proceedings of 17th
International Conference on Artificial Reality
and Telexistence (ICAT 2007).
2007, pp. 23-30.
Yoshimura, M. et al.
(2006). 'Analysis of
Japanese Dance Movements Using Motion
Capture System'.
Systems and Computers in
: 71-82, Translated from Densi
Joho Tsushin Gakkai Ronbunshi, Vol. J87-D- II,

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Conference Info


ADHO - 2010
"Cultural expression, old and new"

Hosted at King's College London

London, England, United Kingdom

July 7, 2010 - July 10, 2010

142 works by 295 authors indexed

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Series: ADHO (5)

Organizers: ADHO

  • Keywords: None
  • Language: English
  • Topics: None