Motion Interpolation Using Adjectives
Masaki Oshita, Maho Katsurada and Aoi Honda
Kyushu Institute of Technology
680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
Email: oshita@ces.kyutech.ac.jp
Abstract—In this paper, we propose a motion interpolation
method using parameters based on adjectives. We conducted
a questionnaire experiment in which subjects were asked toevaluate 10 example walking motions using 41 pairs of adjectives.
Based on the results, we selected 27 pairs of adjectives thatare effective for motion parameterization and determined four
primary parameters by categorizing the adjectives. Our motion
interpolation method allows the user to create various styles of
motions using the four primary parameters and any combinationof the additional 27 adjective pairs. We present the results of ourexperiments and demonstrate the advantages of our method.
Keywords—motion interpolation; adjective; questionnaire experimentI.

I NTRODUCTION

Motion interpolation is a common technique in computer
animation and can be used for generating a new motion froma set of existing motions through some control parameters.
Normally a small number of quantitative parameters such as
the position of the end-effector, walking speed, and direction
are used. However, animators often want to edit motion using
adjectives. For example, they might want to create a motionthat looks happy, depressed, lively, or weak.
In this paper, we propose a motion interpolation method
using parameters based on adjectives. In general, there aremany adjectives, and it is not easy to choose appropriate ones
for motion interpolation. We hence conducted a questionnaire
experiment where subjects were asked to evaluate ten example
walking motions using 41 pairs of adjectives. Based on the
results, we selected 27 pairs of adjectives that are effective formotion parameterization and determined four primary parameters by categorizing the adjectives. Our motion interpolation
method allows the user to create various styles of motion
using the four primary parameters and any combinations of
the additional 27 adjectives pairs. We present the results of ourexperiments and demonstrate the advantage of our method.
Our approach can be applied to any kind of motion. In
this research, we chose walking motion as an example because
walking is a common human behavior that can express various
styles. In addition, it is a cyclic motion and can be played backrepeatedly, which makes it easy to observe during experiments.
We applied our method to 10 example motions, which is arelatively small number. In practice, it is not easy to createa large number of examples of a speciﬁc kind of motion
with many styles. Unlike previous research that required many
example motions, our method works well with this small
number of example motions.
The main contributions of this paper are as follows. We
propose an approach for the quantiﬁcation of adjectives for motion interpolation. We also determined four primary parameters

based on adjectives through our experiments. We developed amotion interpolation system with the four primary parameters
and any combination of adjective pairs. Our approach andthe primary parameters should be applicable to other kindsof motion with various styles.
The remainder of this paper is organized as follows. Section
II reviews related work. Section III describes the quantiﬁcationand classiﬁcation of adjectives based on our questionnaire
experiment. Section IV explains our implementation of motion
interpolation. Section V presents the experimental results anddiscussion. Section VI concludes this paper.
II.

R ELATED W ORK

Motion interpolation techniques are used in computer animation [1], [2], [3], [4], [5], [6]. A motion interpolation
generates a new motion from a set of example motions byblending them. A feature vector is assigned to each example
motion in advance. Given a desired feature vector, the blending
weights of these example motions are computed based on theirfeature vectors. By blending these example motions with theweights, a new motion is synthesized. Several approaches havebeen proposed for computing the blending weights. Rose et al.
[2] combined linear approximation and non-linear adjustmentswith radial basis functions. This approach has been adaptedby many researchers [4]. Wiley and Hahn [1] combined linear
interpolations of nearby examples around the speciﬁed parameter. This approach also has been adapted by other researchers[3], [5]. However, it requires a large number of dense examples
over the parameter space. To solve this problem, Kovar etal. [5] generated many examples by interpolating existing
examples. Mukai and Kuriyama [6] introduced a geostatisticalmodel for statistically estimating the correlations between
feature and motion spaces. Lau et al. [7] modeled a set ofexample motions by their spatial temporal variations. Min et al.
[8] applied principle component analysis to example motions
to construct a low-dimensional statistical model for generating
a motion by determining the blending weights of the principal
components to satisfy the given constraints. In our research,
because our primary contribution is our parameterization of
adjectives and any motion interpolation method can be combined with our system, we use the standard approach [2], [4].
To apply motion interpolation, the feature space must be
deﬁned and feature vectors must be assigned to example
motions. Various kinds of feature vectors have been used inprevious studies. Basically, the dimension of the feature spaceneeds to be small compared to the number of example motions.
Walking speed and turning direction are used as feature vectors
for walking and running motions [2], [4]. The position of theend-effector is used as a feature vector for reaching, punching,Fig. 1.

motion 0

motion 1

motion 2

motion 3

motion 4

motion 5

motion 6

motion 7

motion 8

motion 9

Example motions used in our experiment.

and kicking motions [5], [6]. Rose et al. [2] parameterized
different styles of motions using parameters based on adverbs
such as happy, sad, and angry. Their features are similar toours. However, they used only a small number of adverbs thatthe authors had chosen. In contrast, our method allows theuser to utilize any combination of adjectives and the primaryparameters that are derived from many adjectives. Adverbs
and adjectives have the same role and both can express styles
of motions. In addition, our method can be applied to both.
However, in this paper, we use adjectives rather than adverbs,
because a mixture of adjectives and adverbs is confusing andadjectives are naturally used to express the styles of motions inJapanese, the language in which our questionnaire experiment
was conducted.
Recently, Förger and Takala [9] proposed a method formotion interpolation using verbal expressions and applied it
to walking motion. Unlike our method, instead of verbal
expression input, they used motion features that are computedfrom example motions and are associated with the verbal
expressions as motion interpolation parameters. They used
35 example waking motions and 13 verbal expressions suchas fast, slow, aggressive, lazy, and excited in their experiment. They introduced an incremental and iterative process
for motion interpolation with a pseudo-inverse matrix. Theirapproach requires more motions than the effective motion
features and also requires more manual annotation than our
method. Moreover, as the output motion depends on the series
of inputs, it is difﬁcult to reproduce the same motions.
Quantifying a subjective factor using a questionnaire experiment is a common approach, although it is not straightforward and careful design is required. Komatsu [10] quantiﬁed
adjectives and onomatopoeias, which consist of consonants
and vowels, using a questionnaire experiment. Although hepresented an application for simple robot motion control,
his quantiﬁcation of adjectives was general-purpose and notintended for motion synthesis. Different methods and ex-

periments are required for quantifying adjectives for motion
interpolation.
We applied a hierarchical clustering method [11] to extract
the primary parameters from the various adjectives that areused in our experiment. There are many approaches for extracting a small number of parameters from a large number ofdata. Principal component analysis is one popular technique.
However, it does not suit our purpose, because it would extract
the primary components across all the adjectives, the extracted
components would become irrelevant to the adjectives, andit would be difﬁcult to control these parameters. We rather
choose to classify adjectives into a small number of groups
so that the parameters based on these groups are intuitively
controlled. Recently, machine learning techniques have been
widely applied in many areas. For example, deep learning[12] can extract a small number of latent parameters from alarge number of data by combining layers of neural networks.
However, these techniques require a lot of training data,
which are difﬁcult to collect through a questionnaire-based
approach and thus not applicable to our problem. However,
the hierarchical clustering method [11] works well, even witha small number of data.
III.

Q UANTIFICATION AND C LASSIFICATION OF
A DJECTIVES

We conducted a questionnaire experiment to quantify andclassify adjectives.
A. Motion Data
As explained in Section I, we chose walking motion as
example for this research. We created 10 walking motions using the optical motion capture system OptiTrack. The walking
motions consist of one cycle of straight walking at normal
speed with various styles. A male subject of average body form
was asked to perform various styles of walking. Because weTABLE I.

L IST OF ALL 41 PAIRS OF ADJECTIVES USED IN OURQUESTIONNAIRE EXPERIMENT.

slow - fast
dull - quick
blunt - sharp
weak - strong
dirty - clean
uncool - coolugly-cuteold - young
unstable - stable
artiﬁcial - naturalvague - distinct
poor - richdrab - clear
calm - excited

mild - violent
quiet - noisy
dormant - brisk
unpleasant - pleasantsad - happy
dark - bright
simple - gaudy
heavy - lightsmall - large
closed - open
narrow - broadtight - freegloomy - cheerful

hard - soft
angular - circular
edgy - roundinelastic - elastic
bumpy - smooththin - thickcoarse - ﬁnestrained - relaxedunafraid - afraid
adult - childish
ugly - beautiful
dim - sharp
lonely - bustling
narrow - wide

focus on style control via adjectives, other quantitative factors
such as speed and direction were ﬁxed for all example motions.
Although we tried to create as many variations of walking
motion as possible, we captured walking motions with distinctive styles. Finally, 10 walking motions were created. Figure 1
shows images from these example motions. A single cycle ofmotion can be played back in a loop to generate a continuous
walking animation. These motions are also presented in theaccompanying video.
Our motion data contain the movements of the full body.
They do not contain the movements of the ﬁngers and face.
Motion data are represented by a series of poses. Each pose isrepresented by the rotation of all joints and the position andorientation of the pelvis based on a hierarchical body modelthat is also constructed from the motion capture data. Our bodymodel has 20 joints. The motion data have 30 frames/s.
B. Adjectives
We prepared 41 pairs of adjectives, as shown in Table I.
Normally, an adjective can be paired with another adjectivethat has the opposite meaning. Therefore, we form pairs ofadjectives and treat each pair as one parameter. We selectedadjectives that are commonly used for expressing styles of
motions from the dictionary. With respect to the pairs ofadjectives in [10], our research shares 33 pairs with theprevious work and contains eight new pairs. Ten pairs ofadjectives in [10] were not considered applicable to motion
and thus have been removed from our experiment.
Our questionnaire experiment was conducted in Japanese.
All of the adjectives are in Japanese, and the adjectives inthis paper are translated versions of them. Although adjectives
may not translate precisely from one language to another, weconsider that our approach and results in Japanese can bealso applied to other languages as long as the adjectives aretranslated, because motions are basically universal. Our systemprovides both Japanese and English versions of the interface.
C. Questionnaire Experiment
Fourteen subjects who are computer engineering undergraduates and graduates participated in our questionnaire
experiment. They were asked to evaluate the 41 pairs ofadjectives for the 10 example motions on a 5-point scale (−2,−1, 0, 1 or 2). It took about 30 minutes for each subject tocomplete the task.

Fig. 2. Our system for questionnaire experiment with which the subject
can observe example motions and evaluate each pair of adjectives for eachexample motion.

We developed a simple program to conduct our questionnaire experiment. Figure 2 shows an example screen shot. Eachmotion is displayed on the screen. The subject can select anadjective pair and evaluate it by clicking the corresponding
button on the screen. The colors of the buttons represent thevalues that the subject entered. The subject can also control
the camera to see the example motion from any angle. The
lists of pairs of adjectives are shown on the screen.
D. Quantification of Adjectives
By taking the average of all answers of the subjects, we
obtained the values of each pair of adjectives for each example
motion. Of the 41 pairs of adjectives, the ones that are noteffective for expressing the styles of the example motions were
removed. If the average value of the answers from all subjects
for the j-th pair of adjectives for all example motions is neutral,
the pair of adjectives was considered ineffective. In addition, if
the average distribution of the answers from all subjects for thej-th pair of adjectives for all example motions is high, this pair
was also considered ineffective. These pairs of adjectives were
removed. These conditions were evaluated by the following
equations:(1)μ∗j = max |μij | : i = 0...N ,
N
σ̄j =

i=1 σij,(2)N
where μij is the average value of the answers from all subjects
for the j-th pair of adjectives for the i-th example motion
between −2.0 and 2.0, and σij is its distribution. Further, N
is the number of example motions. The j-th pair of adjectives
was removed if the following condition was satisﬁed:
 


(3)
μ∗j < 1.0 ∨ σ̄j > 0.85 .
These thresholds were determined empirically. They could
be adjusted based on the number of parameters to be used
for motion interpolation. Through this process, 14 pairs ofadjectives were removed and 27 pairs of adjectives remained,as shown in Table II.

The average values between −2.0 and 2.0 were scaled
between 0.0 and 1.0. Finally, we obtained impression matrix
A, which represents the coefﬁcients between the 27 pairs ofadjectives and 10 example motions.
E. Classification of Adjectives
To provide motion style control through a small numberof parameters based on adjectives, we classiﬁed the pairs ofadjectives into a small number of groups. We applied a popularhierarchical clustering method, Ward’s method [11], which
repeatedly combines the two clusters whose distance is thesmallest of all pairs of clusters until a sufﬁcient number ofclusters are obtained. The distance E between two clusters is
deﬁned by the following equations:
ΔE(Gi , Gj ) := E(Gi ∪ Gj ) − E(Gi ) − E(Gj ),d(ai , M (G)),E(G) =

(4)
(5)

ai ∈G

M (G) =

1 
ai ,
|G|

(6)

ai ∈G

where Gi and Gj are clusters, M (G) is the center of acluster and d(ai , aj ) is a distance function. We applied thismethod to the coefﬁcient vectors of the pairs of adjectives
from impression matrix A, obtained in Section III-D. We usedsquared Euclidean distance, which is not a metric function
but is a semi-metric function satisfying the relaxed triangle
inequality, between the vectors as distance function d(ai , aj ).

Fig. 3. Constructed dendrogram representing the hierarchical clusters of 27
adjective pairs.
TABLE II.

adjectives
slow - fast
dull - quick
blunt - sharp
weak - strong
dirty - clean
uncool - coolugly-cuteold - young
unstable - stable
artiﬁcial - naturalvague - distinct
poor - richdrab - clear
calm - excited
mild - violent
quiet - noisy
dormant - brisk
unpleasant - pleasantsad - happy
dark - bright
simple - gaudy
heavy - lightsmall - large
closed - open
narrow - broadtight - freegloomy - cheerful

Ward’s method constructs a tree diagram called a dendrogram to illustrate the hierarchical arrangement of the clusters
by repeating the process until all clusters are merged into one.
Using the dendrogram, the number of clusters can be manually
determined. The dendrogram in Figure 3 was constructed. We
chose to divide the pairs of adjectives into four clusters, as indicated by the dashed line in Figure 3, because they are clearly
separated and each of them contains an adequate numbers ofadjective pairs. Table II shows 27 pairs of adjectives in fourclusters. We labeled these clusters “quickness,” “clearness,”“activeness,” and “largeness.”
Komatsu [10] classiﬁed pairs of adjectives into four groups
labeled “sharpness,” “softness,” “dynamic,” and “largeness”
based on their factors with respect to onomatopoeias. There aresome similarities between his classiﬁcation and ours. However,they do not match exactly, because our classiﬁcation is basedon the styles of example motions, while his classiﬁcation isbased on onomatopoeia. Labanotation [13], which is a notation
system for describing dancing movements, deﬁnes four kindsof effort for the dynamic quality of movements: “space,”“weight,” “time,” and “ﬂow.” They roughly corresponded to
our factors “largeness,” “activeness,” “quickness,” and “clearness,” respectively. However, they do not match exactly either,because labanotation is specialized for dancing motions. These
comparisons indicate that our four-class clustering is supportedby the classiﬁcations that have been designed in other applications and is reasonable.
For motion interpolation, in addition to coefﬁcient matrix
A between the pairs of adjectives and example motions, weneed coefﬁcient matrix A between the four clusters and

L IST OF THE SELECTED 27 ADJECTIVE PAIRS AND THEIR
CLASSIFICATION .
category
quicknessclearness

activenesslargenessexample motions. We obtain them by taking the average of
all the pairs of adjectives in each cluster.
IV.

M OTION I NTERPOLATION U SING A DJECTIVESThis section describes our method for motion interpolation using adjective-based parameters. To implement motion
interpolation, we employed a common method that uses a
combination of linear approximation and radial basis functions[2], [4].

B. Motion Interpolation
Given the parameters p in M -dimensional parameter space,the blending weights w of N motions are computed by a combination of linear approximation and non-linear adjustmentswith radial basis functions. The i-th component of the weights
is computed by
wi =

M

j=0

(a) main screen

lij Lj (p) +Nrik Rk (p),

(7)

k=1

where Lj (p) and lij are the linear basis and coefﬁcients,
respectively. Here, Lj (p) is the j-th component of p
(j = 1...M ) and L0 = 1. The linear coefﬁcients are computedfrom the parameters of the example motions by solving theleast squares problem with the sub-matrix of the coefﬁcientmatrix A from Section III-E. In addition, Rk (p) and rik arerespectively the non-linear radial basis and coefﬁcients. Eachwi is bound between 0.0 and 1.0 and w is normalized. Because
coefﬁcients lij and rij depend on the parameter space, they arerecomputed when the combination of adjectives is changed.
For details of the algorithm, readers may refer to the previous
work [2], [4].
Using the determined blending weights, the example motions are blended. To compute pose q(t) of the output motion
at time t, the corresponding poses qi (ti ) of example motion i
at ti are blended. The corresponding timing for each example
motion ti is computed by applying a time-warping based on
the keytimes of example motions and keytimes of the blended
motions. The keytimes of the blended motions are determinedby taking a weighted average of the keytimes of example
motions with the blending weights.

(b) selection of a pair of adjectives
Fig. 4. User interface of our system for motion interpolation using adjectives.
The user can control the adjective parameters through the sliders on the left
side of the screen.

A. User interface
A user of our method can specify values for the four
primary parameters. In addition, the user can specify any
additional pairs of adjectives and their values. All values are
between 0.0 and 1.0. Although the quantiﬁcation of adjectives
is calculated based on the results of the questionnaire experiment in Japanese, our system also provides an English versionof the interface. The English version provides the translated
adjectives.
Figure 4(a) shows the interface of our prototype. The four
primary parameters are controlled using the sliders on the left
side of the screen. The user can add, delete, and alter a pair ofadjectives by clicking the icons on the bottom and side of thesliders and choosing an item from the list of adjective pairs,
as shown in Figure 4(b). As the user adjusts these parameters,
the motion blending weights on the right side of the screen
are automatically updated and the synthesized motion is alsochanged immediately. Alternatively, our system allows the user
to control the blending weights directly by using the sliders
on the right side of the screen.

V.

R ESULTS AND D ISCUSSION

A. Experiment
To evaluate the validity and effectiveness of our method,
we conducted an experiment. For each trial of the experiment,a target motion was randomly generated using our system. The
subjects of our experiment were asked to create a motion thatis similar to the presented target motion using our interface andalso by adjusting the blending weights directly for comparison.
A video of ten example motions was also presented to thesubjects on the other screen to help them to adjust the blending
weights.
For each target motion, the subjects were asked to complete
the following three steps, as shown in Figure 5.
1)

2)

3)

They were asked to determine the adjective parameters and blending weights without seeing the synthesized motions. The distances between the target
and synthesized motion for the adjective parameters
and blending weights, Dp and Dw , respectively, were
measured.
They were asked to create a motion that matched thetarget motion by adjusting the blending weights, if
they could not generate it with their initial guess. The
required time Tw to complete this step was measured.
In the same way, they were asked to create a motionthat matched the target motion by adjusting the adjective parameters. The required time Tp was measured.

(a) step 1: estimating both parameters
Fig. 5.

(b) step 2: adjusting blending weights(c) step 3: adjusting motion parameters

Screen shots of our experiment. The green ﬁgure represents the target motion generated by our system. The blue ﬁgure represents the created motion.

For steps 2 and 3, if a subject could not recreate the motion
in 300 seconds, the step was terminated. We introduced thistime limit because subjects could not reproduce the target
motion, no matter how long they tried when the target motion
was difﬁcult to create. We also collected comments from thesubjects after the experiment.
The target motions were generated randomly as follows.
Some primary adjective parameters were randomly given andother parameters were set to neutral (0.5). A target motion wasthen generated based on the adjective parameters. To avoid
creating an easy target motion that could be recreated withoutchanging the adjective parameters at all, if the generated
motion was close to the neutral motion, which is generated
by setting all parameters to neutral values (0.5), this process
was repeated to create a target motion that was not close to theneutral motion. We created four kinds of target motions (Levels
1, 2, 3, and 4) depending the number of primary adjective parameters that were changed. A Level 1 target motion should be
easily reproduced by adjusting one primary parameter, whilea Level 4 target motion could require adjusting four primaryparameters and should be difﬁcult to reproduce. However, thisalgorithm was disclosed to the subjects. Therefore, they may
have adjusted more parameters than necessary. Although we
could generate target motions by randomly determining theblending weights, we did not take this approach, because we
wanted to control the level of difﬁculty, and most of the
motions were possible to recreate through the four primaryadjective parameters. We discuss this further in Section V-C.
Nine subjects who are computer engineering undergraduates and graduates participated in our experiment. None ofthese subjects had experience in making computer animation.
Before the experiment, the subjects were told how the interfaces worked and given enough time to practice. Each subject
was asked to create one target motion from each level. Hence,
they had four trials in total. It took about 30 minutes for eachsubject to complete the experiment.
Steps 2 and 3 of each trial were completed when therecreated motion matched the target motion and the distance
between them became lower than a threshold. Whether the two
blended motions look similar cannot be simply determinedbased on their blending weights because different blending
weights may generate similar motions. Therefore, we used theaverage distances of the positions of all joints over the two

motions. Distance D was computed by

 
 
J 
T 

t 
 target t
− xj
D=
x j
,


T
T
t=0 j=1(8)

where T is the number of frames, J is the number of joints,
and xtarget(t ) and xj (t ) are the position of the j-th joint
jat normalized time t of the target and generated motions,
respectively. Because the durations of the two motions aredifferent, we used the normalized time and poses at keytimes.
In our implementation, T is 10 and J is 20. The threshold was
set to 0.06 m.
B. Results
The distances between the motion created by an initial
guess using the two interfaces and target motion Dp , Dw
are shown in Figure 6. There was no signiﬁcant distance
between the results of the two interfaces. We performed a
Wilcoxon signed-rank test to compare the results of the twointerfaces. Because data were taken from the same person forthe same target motion, they are paired data connected witheach other. The Wilcoxon signed-rank test is a nonparametricpaired difference test that is used as an alternative to thepaired Student’s T-test when the data cannot be assumed
to be normally distributed. The null hypothesis is that themedian difference between the pairs of results of two interfaces
is zero. The results of the Wilcoxon signed-rank test werep = 0.578, 0.499, 0.441, 0.878, and 0.411 for Levels 1, 2, 3,
and 4 and all levels, respectively. In all cases, p is larger
than the signiﬁcance level of 0.05, which means that thenull hypothesis is not rejected and there was no signiﬁcantdifference between the pairs of results. In addition, the ratioof subjects who recreated the target motion with an initial
guess is shown in Figure 7. The results show that the subjects
reproduced the target motions by using adjective parameters inmany cases compared with using the blending weights, eventhough there was no difference in the average distances. Thisis probably because using adjective parameters worked very
well when the target motion matched the adjective parametersthat the subject had thought of.
The times required to create the target motion from the initial guess Tp and Tw are shown in Figure 8. Figure 9 shows
the reduction in time when using our method compared withthe time required when adjusting the blending weights. This is350.00

0.35

300.000.3
250.000.25

200.00150.000.2

100.00
0.15

50.00
0.00

0.1

-50.00Level 1

0.05

Level 2

blending weights

Level 3

Level 4

All Levels

adjecve parameters (our method)0
Level 1

Level 2
blending weights

Level 3

Level 4

All Levels

adjecve parameters (our method)

Fig. 8. Average time required to create the target motion using both interfaces.
Error bars represent one standard deviation, which is calculated under theassumption of independence between paired data.

Fig. 6. Average distance to the target motion from an initial guess createdusing both interfaces. Error bars represent one standard deviation, which is
calculated under the assumption of independence between paired data.

350.00300.00250.00100

200.0090
150.008070100.00

60

50.00

50

0.00

40

-50.0030-100.00

20-150.00

10

Level 1

Level 2

Level 3

Level 4

All Levels

0
Level 1

Level 2
blending weights

Fig. 7.
guess.

Level 3

Level 4

All Levels

adjecve parameters (our method)

Ratio of subjects able to recreate the target motion with an initialexpressed as the mean of the deduction from the required timeby using the blending weight and the 90% interval. From thisﬁgure, we can deduce that the mean of the required time byusing adjective parameters is less than when using blending
weights at a 95% conﬁdence level in Levels 1 and 4 and alllevels. In addition, the results of the Wilcoxon signed-rank test
were p = 0.011, 0.866, 0.173, 0.007, and 0.000 for Levels 1,
2, 3, and 4, and all levels, respectively. There were signiﬁcantdifferences for Levels 1 and 4 and all levels. The ratio of
failures, which means that the subject could not make the target
motion in time (within 300 seconds), are shown in Figure 10.
Clearly, the subjects could not make the target motion manytimes when they used the blending weights. The failure data
are approximated by 300 seconds in the results in Figures 8 and9. We remark that they should be more precisely approximatedby some statistical methods, but approximation as 300 seconds
does not disadvantage the method using the blending weights.
Adjective parameters required less time to use than blending
weights. This is probably because using adjective parameters
was intuitive for the subjects and the number of parameters tobe adjusted was small.

Fig. 9. Average difference between the time required to use our method andthat required to adjust the blending weights.

There were no signiﬁcant differences between the results
for all four levels. Because the algorithm for generating target
motion was not explained to the subjects, they tried to adjust all
four primary adjective parameters at every level. Therefore, thetarget motions at the lower levels were not easier to create than
the target motions at any of the higher levels. Most of subjects
mainly used only the four primary adjective parameters. There
were some subjects who used additional adjective parametersactively.
The subjects commented that using the primary adjective
parameters was intuitive and they were able to create theintended motions easily. The subjects also mentioned that using
the blending weights might be effective for creating a target
motion that is close to one of the example motions, but mostly
it was difﬁcult to adjust all blending weights to create a desired
motion.
These results indicate that our adjective parameter-based
interface is better than the interface that adjusts blending
weights. In this experiment, the number of example motions
was 10, which is relatively small. In practice, more example
motions can be used, which makes adjusting the blending
weights much harder. Our adjective parameter-based interface
is effective for controlling motion style, which is difﬁcult tovarious styles of motion can be controlled thorough intuitive
adjective-based parameters from a number of precreated example motions. We applied our method on walking motions.
Experimenting our method on other kinds of motions is our
future work. Our method requires a questioner experiment toquantiﬁes adjectives for the target set of motions. We hope todevelop a method for estimating the adjective-based featuresby analyzing motions. This is also our future work.

100
90
80
70
60
50
40
30
20

ACKNOWLEDGMENT

100
Level 1

Level 2
blending weights

Fig. 10.

Level 3

Level 4

All Levels

adjecve parameters (our method)

Ratio of failures to make the target motion.

parameterize. As mentioned above, most subjects used only
the primary adjective parameters. A few subjects commented
that the primary adjective parameters were too abstract andthe additional adjective parameters were more intuitive. In
contrast, a few other subjects commented that there are somany additional adjective parameters that it is difﬁcult tochoose an appropriate one from them. Our interface allows
any combinations of the primary and additional adjective
parameters. A user of our interface can choose the approachthat they prefer. This is an advantage of our interface.
C. Discussion
In this experiment, most motions that can be created byour system are possible to create through the four primaryadjective parameters without additional adjective parameters.
This is probably because the degree of freedom of styles in the
10 example motions was about four, that is, not particularlyhigh. Therefore, the additional adjective parameters were notused to generate target motions in our experiment.
Our results are based on a set of example motions and alimited number of subjects. Different results may be achieved
from a different group of subjects. However, the four primaryadjective parameters in our results are evident, as discussed
in Section III-E. Therefore, they would be applicable to adifferent group of subjects.
In this research, our method is applied to walking motion.
However, it could be applied to various kinds of motions. The
four primary parameters are also considered suitable for otherkinds of motion. The application of our method to other kindsof motion and other languages is a task for future work. When
our method is applied to other kinds of motion, based on our
ﬁndings, it is possible to conduct a questionnaire experimentwith just the four primary parameters to reduce the effortof the subjects. However, a quantiﬁcation process by humansubjects is still required. It should be possible to estimate
these parameters by analyzing motion data. The automaticquantiﬁcation of example motions for adjective parameters is
a possible direction for our future work.
VI.

C ONCLUSION

In this paper, we proposed a motion interpolation method
using the parameters based on adjectives. Using our approach,

The authors would like to thank Yosuke Higaki and Genki
Ohishi for their help in conducting experiments. This work
was supported in part by Grant-in-Aid for Scientiﬁc Research(No. 15H02704 and No. 15K05003) from the Japan Society
for the Promotion of Science (JSPS).
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