Synthesizing Realistic Image-based Avatars
by Body Sway Analysis
Masashi Nishiyama, Tsubasa Miyauchi, Hiroki Yoshimura, Yoshio Iwai
Graduate School of Engineering, Tottori University
Tottori , Japannishiyama@eecs.tottori-u.ac.jp
Human-­‐like	
  body	  sway	

ABSTRACT

We propose a method for synthesizing body sway to give
human-like movement to image-based avatars. This method
is based on an analysis of body sway in real people. Existing
methods mainly handle the action states of avatars without
sufficiently considering the wait states that exist between them.
The wait state is essential for filling the periods before andafter interaction. Users require both wait and action states
to naturally communicate with avatars in interactive systems.
Our method measures temporal changes in the body sway
motion of each body part of a standing subject using a singlecamera video sequence. We are able to synthesize a newvideo sequence with body sway over an arbitrary length oftime by randomly transitioning between points in the sequencewhen the motion is close to zero. The results of a subjective
assessment show that avatars with body sway synthesized byour method appeared more alive to users than those using
baseline methods.

Wait	
  stateImage-­‐based	
  avatarAc*on	
  state	  UserImage-­‐based	
  avatar

User

Figure 1. We consider the body sway of an image-based avatar in thewait state to enhance the nonverbal communication between it and auser in an interactive system.

time and place (e.g., at an automatic information desk late atnight). In this paper, we focus on synthesizing human-likerealistic avatars by exploiting an image-based technique [2, 9]
in which life-sized video sequences interact with users throughlarge-sized displays.

ACM Classification Keywords

H.5.m. Information Interfaces and Presentation (e.g. HCI):
Miscellaneous
Author Keywords

Interactive systems that use human-like avatars are an active
topic in human-agent interaction research, and have manypotential applications, such as conversational avatars in a museum [15], avatars talking about the past [2], and speechinteractive guidance avatars [10]. These avatars have the potential to actively utilize the large-sized displays that we often
see many places, e.g., in stations, shopping plazas, officeentrances, and airports. These interactive avatars can automatically communicate with users without the constraints ofIn order to synthesize realistic avatars for interactive systems,
we need to consider two states. The first is the action state(e.g., speaking with a user) and the second is the wait state(e.g., maintaining a standing pose). In particular, the wait stateplays an important role in the interaction between avatars andusers because the avatars do not continuously communicate
with the users. The wait state is required to handle periods
before and after interaction with users. For instance, if the
wait state is not adequately applied in the avatars, the users
cannot judge whether to begin communication with the avatars
or not. However, existing methods [2, 9] do not take the waitstate into consideration; they mainly handle the action state.

This is the draft version of our paper of HAI 2016.

Our key idea is to exploit the wait state of the avatars to enhance the nonverbal communication between users and avatars,
as illustrated in Figure 1. To achieve this, we synthesize
human-like micro movements. Consider a standing pose in
the wait state; we often see standing receptionists at airports,
hotels, and information offices. When people maintain a standing pose, they continuously move their body slightly aroundits center line. As described in [11], people unconsciously

Image-based Avatar, Body sway, Human-like movement
INTRODUCTION1

m t : Temporal mask image	 m r : Reference mask image	 mtspread the burden of standing across their muscles by making
micro movements to avoid overloading one set of muscles.
This physiological phenomenon is called body sway.

Sway to the left	

In this paper, we propose a novel method for synthesizing
human-like body sway for image-based avatars in the waitstate. To analyze the body sway of real people, we first acquire video sequences of standing subjects and measure themovement of each body part. We extract the characteristics
of body sway from the temporal changes of this movement.
Using these characteristics, we can synthesize image-based
avatars with body sway for an arbitrary length of time using
identified transitioning times in the video sequences.

Sway to the right	

m r − mtm r − mt

Figure 2. A reference mask image and temporal mask images. Red represents a region converted from body to background and blue represents
the opposite case.

RELATED WORKInteraction systems need to have an intuitive feel so that users
can naturally communicate with avatars as if they were communicating with real people. As described in [6, 7], to design human-like avatars for interaction systems, video synthesis and speech synthesis are key techniques. In particular, video synthesis techniques are directly linked to theappearance of the avatars in nonverbal communication [3,5, 4]. Researchers have developed various video synthesis
techniques for generating realistic avatars, and there are currently two main approaches. The first exploits a computer
graphics-based avatar [15, 10] and the second exploits animage-based avatar [2, 9]. In general, the appearance ofimage-based avatars is more human-like than that of computer graphics-based avatars. However, image-based avatars
can only replay video sequences stored in databases. Thus,
image-based avatars sometimes cannot work well when users
perform unexpected actions. To overcome this difficulty, atechnique [12, 1, 14, 17, 8] that synthesizes various video
action sequences has been proposed. Some methods [12, 1]
synthesize facial video sequences in conversation. Okwechime
et al. [14] proposed synthesizing full-body video sequences ofactions in conversation. Shi et al. [17] proposed editing video
sequences of interactive actions between subjects, and Huang
et al. [8] developed a method to seamlessly combine various
actions. However, these existing methods do not sufficiently
consider synthesizing video sequences of the wait state. Our
method tackles the synthesis of realistic image-based avatars
that contains human-like body sway.

Wang et al. [19] inferred the position of the center of gravityof the whole body using multiple video cameras and Yeunget al. [21] inferred the center of gravity position from body
joints using common gaming sensors. However, these existing
methods handle only the center of gravity position of the bodyand insufficiently consider the micro movements of each bodypart. We aim to measure the amounts of movement of each
body part using a simple video camera without body-attachedsensors. The details of our method are described below.
Algorithm for measuring body sway

Our method uses a binary mask image in which a pixel within abody region is 1; otherwise it is 0. We acquire a video sequenceof N frames from a standing subject. Given a reference time r,
representing the center of the body sway in the video sequence,
we determine a reference mask image mr . We compare thereference mask image with temporal mask images mt (t ∈1, ..., N), as illustrated in Figure 2. Our method computes theamount di of body sway movement as
di =∑(mr (x) − mt (x)) ,(1)x∈parts(i)where mr (x) and mt (x) indicate the pixel value at position x
in mr and mt , respectively, and parts(i) is a body part region
indicated by i, such as the regions shown in Figure 4. The
value of di is positive when the number of pixels converted
from the background to the body is higher and negative when
the number of pixels converted from the body to the background is higher. Note that we compute reference time r using
Algorithm 1 to determine reference mask image mr .

ANALYSIS OF BODY SWAY
Design of measurement of body sway

Here, we consider a scenario in which standing people arealways visible to the public, such as receptionists in public
spaces. In order to synthesize this kind of body sway onhuman-like avatars, we need to measure the movement of each
body part of a person in a standing pose. We also need tocheck whether the body parts move synchronously or not.

Algorithm 1 Determine reference time r.
for r̃ = 1 to N doDr̃ ← 0for t = 1 to N do
compute d˜i using mr̃ , mtDr̃ ← Dr̃ + ∑ |d˜i | for all body partsend forend forr ← arg min Dr̃

To analyze the body sway of real people, a footing force
plate is widely used. However, this device measures temporalchanges of the body’s center of gravity and cannot acquire themotion of specific body parts. Nashner [13] measured sway
motions using acceleration sensors attached to the body. Thesesensors have the disadvantages of needing to be attached toa user and are difficult to temporally synchronize. Recently,2

Green back screen

200 cm90 cm

Phase (deg.)

Subject
Coherence

Camera

180

1

20 cm

Video sequence
Figure 3. Setup for body sway measurement.

0

Left head	
Left shoulder	

Right head	

Frequency (Hz)

-180

0.2

Head_Shoulder00Frequency (Hz)Head_Hand

0.2

Head_ShinRight shoulder	

Left hand	Right hand	Left shin	Right shin	

Figure 5. Coherence analysis of movements (180 s) of right body parts ofsubject A. The values of phase between these parts were nearly identicalthough the values of coherence were different.
Test Subject

Figure 4. Body part regions for measuring body sway movement.

Evaluation of body sway measurement

To evaluate the effectiveness of our method, we captured fivevideo sequences of five test subjects (average age 21.4 ± 0.5
years, average height 166.9 ± 7.1 cm). Each test subject stoodin front of a camera. Each sequence lasted 180 s. We used avideo camera with 30 frames per second and 1, 920 × 1, 080
pixel resolution. We set the camera at a height of 90 cm anddistance of 200 cm from the subject (Figure 3). The boundingbox of the subjects is hence about 300 × 950 pixels. We
generated mask images from the sequences with a simple
background subtraction technique using a green back screen.
We manually identified the head, shoulder, hand, and shinregions (Figure 4) for each video sequence. We set the size ofeach body part to 70 × 70 pixels.

Body part

A

B

C

D

E

Avg.

Left head
Right head
Left shoulder
Right shoulder
Left hand
Right hand
Left shin
Right shin

289
296
205
232
212
191
73
89

132
107
114
86
75
63
31
32

185
191
157
140
145
88
54
50

237
265
156
198
111
107
56
53

197
200
159
155
119
126
57
55

208
212
158
162
132
115
54
56

Table 1. Standard deviation [pixel] of body sway movement |di | for each
body part.

appear and the temporal changes between the zero-crossing
points forms a multi-modal wave. We observed the same tendency for all subjects. We believe that the temporal changes
are periodic and contain the sway motion between the reference times.

Table 1 shows the standard deviation of body sway movement|di | for each body part of each subject. We can see that theseamounts were dynamically different between subjects. We
can also see that the movements of the upper body parts werelarger than the those of the lower body parts. We believe thatall body parts synchronously moved in all subjects. We also
observed the same tendency in Figure 5. Note that a change in
di of about 380 pixels corresponds to a body part movementof 1 cm.

Measurement error evaluation

We evaluated the error of our motion measurement. We used
five reference mask images of the five subjects and 15 temporalmask images randomly selected from the video sequences. We
manually calculated the ground truth of the body regions togenerate the mask images. Figure 8 compares the amountof movement |di | using the ground truth with that measured
using the automatically extracted mask images. These results
show that the error of measurement is very small for each bodypart. We thus believe that the results of our method are notaffected by error from the mask images generated using thebackground subtraction technique.

Figure 6 shows the temporal changes in the amount of movement for each body part. We can see that the temporal changes
were different for each subject because the shape of the waves
notably varies between subjects. We can also see that thetemporal changes were nearly identical for all body parts; forinstance, the head moved to the left and then the other parts
moved in the same direction. Similarly, when the right parts
moved to the left, the corresponding left parts moved in the
inverse direction. We observed this same tendency among all
subjects. We believe that the motion synchronously changes
for all body parts.

Next, we compared the performance of our method with thatof an existing method [21] that measures the 3D center ofgravity using the body joints of Microsoft Kinect v2. We
computed the 2D positions of the centers of gravity projectedfrom the 3D positions because the purpose of our method is tomeasure the 2D variations of body sway in a video sequence.
We compared the Root Mean Squared Error (RMSE) between
the centers of gravity of manually labeled body regions andthose of our method. The RMSE of our method is 1.1 pixels
and that of the existing method is 36.5 pixels. Hence, ourFurthermore, we verified the temporal characteristics of the
movement. Figure 7 shows temporal changes over 60 s for theleft side of the head of subject A. We can see that the zerocrossing points (representing the reference times) repetitively
3

600	

Movement [pixel]	

Movement [pixel]	

1500	

0	

-1500	
0	

-600	
0	

180	

Time [s]	
(a) Left body parts of subject A	180	

Time [s]	
(b) Left body parts of subject B	

600	

Movement [pixel]	

Movement [pixel]	

1500	

0	

-1500	0	0	

: Head	

-600	
0	

180	

Time [s]	
(c) Right body parts of subject A	

0	

180	

Time [s]	
(d) Right body parts of subject B	: Shoulder	: Hand	: Shin	Figure 6. Measuring temporal movement caused by body sway for each body part.

600
Absolute average
movement [pixel]

Movement [pixel]	

1500	

0	
: Reference time	Time [s]	

: Our method3000: Sway	-1500	
0	

: Ground truth

Head

Shoulder

Hand

Shin

Figure 8. Performance of movement measurement using the simple
background subtraction method. Error bars represent standard deviations.

60	

Figure 7. Temporal characteristics of body sway motion. First is that
there is a plurality of reference times and second is that there is sways
between the reference times.

method outperforms the existing method when measuring the
2D positions of body part centers of gravity.
between the reference times, as illustrated in Figure 9. Using
this process, we can synthesize a body sway video sequenceof arbitrary time length. However, we only handle the mask
image representing the shape of the body in the discussion
in the above section. This causes the problem that incorrectreference times are extracted, i.e., the shape of the body issimilar but its texture is not. In order to synthesize a humanlike body sway, we should take both the appearance and shapeof the body into consideration. We also exploit the appearanceof the face because users are sensitive to facial expressions.
The details of this synthesis are described below.

SYNTHESIS OF IMAGE-BASED AVATARS WITH HUMANLIKE BODY SWAY
Design for generating body sway

We now describe how we synthesize body sway for imagebased avatars. We consider exploiting the characteristics ofthe temporal changes of movement shown in Figure 7. Our
method extracts the reference times of body sway from a video
sequence captured from a subject for an image-based avatar
and replays the video sequence by randomly transitioning
4

r2

r1

r3

r4

Algorithm 2 Determine initial reference time r0 .
for r˜0 = 1 to N do
Sr˜0 ← 0
for t = 1 to N do
compute s̃t using mr̃0 , ar̃0 , fr̃0 , mt , at , ft
Sr˜0 ← Sr˜0 + s̃t
end forend forr0 ← arg max Sr˜0

rk
Time	

Figure 9. Synthesizing a body sway video sequence using a set of reference times rk . Our method replays a video sequence of body sway byrandomly transitioning between the reference times. The black arrows
represent the flow of the original video sequence and blue arrows represent the flow of the synthesized video sequence.

st reaches a local maximum and is greater than a threshold T1 .
To prevent extracting reference times with short intervals, we
also add a condition that reference times are selected morethan an interval threshold of T2 apart.

Algorithm for computing shape and appearance similarities

To synthesize body sway, our method computes the similarityof the body shape, body appearance, and face appearance ateach time t. We use mask image mt to represent body shape,
color image at to represent body appearance, and facial image
ft to represent facial appearance. Our method generates mt
using the same technique described in the section on measurement. Here, at refers to each frame of a video sequence. We
generate the pose-aligned ft using facial feature points inferred
by a face tracking technique [16]. Given an initial referencetime r0 , our method computes similarity st,s of a body shapeasst,s = e−λ ∑ |di | ,Algorithm 3 Generate set of reference times.
for t = 1 to N do
compute st using mr0 , ar0 , fr0 , mt , at , ft
if st is local maximum, st > T1 , interval > T2 then
add time t to set of reference times
end ifend for(2)

We next describe a video transition technique using the set ofreference times (Algorithm 4). We randomly select a referencetime rk from the set and display color images at from theselected reference time rk to the next reference time rk+1 . By
repeating the random selection, we can synthesize realistic
video sequences of body sway for image-based avatars.

where λ is a constant and di is the amount of motion measured
using mr0 and mt . Equation (2) uses the summation of difor all body parts. We use st,s to extract the times that shifts
between body and background regions do not occur. Next, we
compute the similarity st,a of body appearance as
st,a = SSIM(ar0 , at ) ,(3)

Algorithm 4 Synthesize new video sequence.
while true doselect rk randomly from set of reference times
for t = rk to rk+1 do
display color image at
end forend whilewhere SSIM is the Structural SIMilarity [20], used to predictthe perceived quality of frames of a video sequence. We use
st,a to extract the times of unvarying texture over the wholebody. Furthermore, we compute similarity st, f for the face
appearance asst, f= FaceSimilarity(fr0 , ft ) ,(4)

where FaceSimilarity is a correlation value using the edgebased feature vectors of fr0 and ft . We use st, f to extract thetimes of unchanging facial expressions such as between blinks.
We compute the final integrated similarity st asst= αst,s + β st,a + γst, f ,Evaluation of synthesis of body sway

We evaluated the body sway of image-based avatars synthesized by our method. We used male and female avatars. We
acquired video sequences of 90 s for each standing person,
extracted eight reference times the video sequences and synthesized new sequences of 20 s video to generate the avatars.
We set α = 0.5, β = 0.25, γ = 0.25, λ = 1.0×10−5 , T1 = 0.97,
and T2 = 1. The parameters were experimentally determined
to gain smoothness of synthesized videos. We conducted thesubjective assessment on the following methods:(5)

where α + β + γ = 1. Using st , we aim to extract the reference
times at which the shapes and appearances do not changemuch over the whole-body. Note that we determine initialreference time r0 using Algorithm 2.
Transition between reference times

V1: (Ideal) directly replaying the first of 20 s of the acquired
video sequences

As described above, the reference times are suitable for seamlessly transitioning through the video sequence. We thus exploit the set of reference times rk to synthesize human-likebody sway. Our method generates the set of reference times
by searching for times at which the shape and appearance ofthe body are close to each other (Algorithm 3). At these times,

V2: (Our method) replaying the synthesized video sequences
V3: (Baseline 1) replaying one short video sequences of 4 s
randomly chopped from the acquired video sequences5closely represented human-like movement. Figure 14 shows
the comparison of the number of votes between our method
and the alternative method. Our method obtained 18 votes andthe alternative method obtained 2 votes. We believe that thechoosing random transitions at reference times is effective forsynthesizing human-like movements.

80-inch display

Subject

100 cm50 cm

CONCLUSION

We proposed a method for analyzing and synthesizing bodysway to generate human-like wait states of image-based
avatars. By measuring the amount of movement for each
body part, we observed that reference times of body sway repeatedly appear and all body parts continuously move between
these reference times. We synthesized an arbitrary length timeof body sway by randomly transitioning between the reference
times. A subjective experiment demonstrated that our method
is more agreeable than the baseline methods for subjective
assessment.

Figure 10. Setup for the subjective assessment.

Our	
  method	  V3 V2 V1

V4
-3	
-­‐3

-2	
-­‐2

Disagreeable	

-1	
-­‐1

0	
0

1	
1

22 	

Agreeable	

As part of our future work, we will expand our analysis on
interactive systems and intend to consider the transitions between wait and action states.

Figure 11. Results of the subjective assessment of synthesized body swayof avatars. We used the paired comparisons method.

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V4: (Baseline 2) displaying a single shot from the acquired
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V2: Our method	

Time transition of a video sequence	Time transition of a video sequence	

V1: Ideal	

V3: Baseline 1	

V1: Ideal	(a) Male avatar	

V2: Our method	

V3: Baseline 1	(b) Female avatar	

Figure 12. Comparison of synthesized video sequences of a male and a female avatars. Red represents a region converted from body to background andblue represents the opposite case.

7

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Figure 13. Video sequences using the alternative method. The same
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5	
  

10	
  
15	
  
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20	
  

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Figure 14. Comparison of the number of votes between our method andthe alternative method in subjective assessment.

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