Exploration of a Stylistic Motion Space Through Realtime Synthesis

elle Tilmanne, Nicolas d’Alessandro, Maria Astrinaki and Thierry Ravet

numediart, Institute for Creative Technologies, University of Mons, Mons, Belgium

Keywords:

Motion Capture, Motion Style, Realtime Synthesis, Hidden Markov Models.

Abstract:

We present a ﬁrst implementation of a framework for the exploration of stylistic variations in intangible her-

itage, recorded through motion capture techniques. Our approach is based on a statistical modelling of the

phenomenon, which is then presented to the user through a reactive stylistic synthesis, visualised in real-time

on a virtual character. This approach enables an interactive exploration of the stylistic space. In this paper, a

ﬁrst implementation of the framework is presented with a proof-of-concept application enabling the intuitive

and interactive stylistic exploration of an expressive gait space.

1 INTRODUCTION

Preservation of patrimony, and more especially of in-

tangible cultural heritage (ICH), could gain a com-

pletely new dimension thanks to ICT which have ap-

peared and matured in the last decades. Human ex-

pert gesture is the essence of most expressions of

ICH: dance performances, craftsmanship, music per-

formances, etc. The development of motion capture

(mocap) technologies, becoming more precise, less

invasive and more affordable, has hence logically set

it as an unavoidable approach to intangible patrimony

preservation. Mocap enables the transformation of

tridimensional human movements into a digital form.

This transformation is made through the approxima-

tion of the complex human skeleton and body by a

simpliﬁed kinematic chain of body segments or a re-

duced set of body joints. Different technologies coex-

ist, each one with its advantages and drawbacks, but

solutions can be found for most problems, and it be-

comes a common tool for performance capture.

However patrimony preservation should aim at

preserving a global know-how, not one random oc-

currence or expression of the performance, nor a re-

stricted subset of the overall patrimony. Several rep-

resentative performances should hence be recorded,

ideally from different performers, or expressing dif-

ferent styles if applicable. Unfortunately this global

know-how, which can also be seen as a the intangible

heritage which should be preserved, is hard to capture

and to present in a meaningful way. Three main lim-

itations arise with the use of mocap technologies for

capturing expert gestures in performances.

The ﬁrst one appears when aiming at preserv-

ing the overall know-how through several record-

ings, from several performers, with different styles, as

the amount of mocap data can rapidly become quite

huge. Inter and intra subject variabilities, stylistic

factors and external factors inﬂuence each recording,

and the dimensionality of mocap data itself is high.

The amount and high variability of the recorded data

makes it hard to analyse and to use.

A second limitation comes from the lack of in-

teractivity when playing back prerecorded motions.

Mocap data is a recording of a performance. There is

hence no interactivity when viewing it again. If a high

number of performances have been recorded, brows-

ing the motion database can be quite difﬁcult and not

intuitive, as the user gets lost in the contents.

The third major issue with mocap is the repre-

sentativity of the data itself. Cartesian coordinates

of body joints or angles between body segments are

concepts which are difﬁcult to apprehend and do not

match our human experience and expertise of body

motions. The difﬁculty to interpret the evolution of

tridimensional angles or coordinates over time make

it necessary to visualise mocap data through their pro-

jection on 3D virtual characters.

The approach presented in this paper aims at coun-

terbalancing these drawbacks by proposing an alter-

native tool for visualising and understanding high-

dimension mocap databases that exhibit deliberate

stylistic variations. Our stylistic exploration tool is

based on statistical models which can be considered

as a summary of the mocap data, and are used for

realtime synthesis of motions with reactive stylistic

803

Tilmanne J., d’Alessandro N., Astrinaki M. and Ravet T..

Exploration of a Stylistic Motion Space Through Realtime Synthesis.

DOI: 10.5220/0004876008030809

In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (IAMICH-2014), pages 803-809

ISBN: 978-989-758-004-8

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

control. The visualisation of the data is presented

through projection of the synthesised motion curves

onto a virtual character. This paper presents a proof-

of-concept of this motion style exploration tool ap-

plied to the case study of walk. In Section 2, we give

an overview of the statistical modelling that we used

in this research. Section 3 focuses on the realtime

and reactive parameter generation strategy, while Sec-

tion 4 presents the overall application design. Then

some discussion about the results is given in Section 5

and Section 6 concludes.

2 STATISTICAL MODELLING OF

MOTION CAPTURE DATA

2.1 Motion Database

The case study presented in this paper explores a

stylistic space of human gait. The models were

trained thanks to the Mockey stylistic walk database

(Tilmanne and Ravet, 2010), which aims at studying

the expressivity of walk motions. In this database,

a single actor was recorded walking back and forth

while adopting eleven different “styles”. These styles

corresponded to different emotions, morphology per-

soniﬁcations, or situations, and were arbitrarily cho-

sen because of their recognizable inﬂuence on walk,

as illustrated in Figure 1. The acted styles were the

following: proud, decided, sad, cat-walk, drunk, cool,

afraid, tiptoeing, heavy, in a hurry, manly.

Figure 1: Four example postures from the Mockey database.

From left to right: sad, afraid, drunk and decided walks.

The motion was captured thanks to an inertial mo-

cap suit, the IGS-190 from Animazoo (Animazoo,

2008), containing eighteen inertial sensors. The mo-

tion data is described by the evolution over time of

the 3D Cartesian coordinates of the root of the skele-

ton, along with the eighteen 3D angles corresponding

to the orientation of the skeleton root and of the sev-

enteen joints of the simpliﬁed skeleton used to rep-

resent the human body (Figure 1). The global po-

sition of the skeleton was discarded in our applica-

tion, since it is extrapolated in the Animazoo system

based on the angle values, and can be recalculated

in the same way after synthesis. Each body pose is

hence described by 18 ∗ 3D = 54 values per frame.

We chose to model the rotations of the eighteen cap-

tured joints rather than the 3D Cartesian coordinates

of these joints in order to ensure that the ﬁxed limb

length constraints were respected in the synthesised

motion: as only rotations are applied to the ﬁxed limb

length skeleton deﬁnition, there will be no length de-

formation of the limbs after synthesis. We converted

the 3D angles from their original Euler parameterisa-

tion to the exponential map parameterisation (Grassia,

1998), which is locally linear and where singularities

can be avoided. The motion data was captured at a

rate of 30 fps. The walk sequence were annotated

into right and left steps, thanks to an automatic seg-

mentation algorithm based on the hips joint angles.

These two class labels correspond to the basic mo-

tions which will be represented in our walk model.

2.2 Hidden Markov Models

Hidden Markov Models (HMMs) are widely used for

the modelling of time series, and have been used for

motion modelling and recognition since the nineties.

One of the advantages of using HMMs is that they

exempt from using time warping, needed in most ap-

proaches in order to align sequences prior to anal-

yse them or extract the style component among them.

HMMs integrate directly in their modelling both the

time and the stylistic variability of the motion, thanks

to their statistical nature. In addition to the recogni-

tion applications, the last decade has seen a rising in-

terest for the use of HMMs for generation, especially

with the development of tools such as the HTS Toolkit

developed for speech synthesis (Tokuda et al., 2008).

A HMM consists of a ﬁnite set of states, with tran-

sitions between the states governed by a set of prob-

abilistic distributions called transition probabilities.

Each state is associated with an outcome (more gener-

ally called observation) probability distribution. Only

this observation is visible, the state is called hidden

or latent: at each time t, the external observer sees

one observation o

, but does not know which state

produced it. HMMs are double stochastic processes,

since both the state transitions and the output distri-

butions are modelled by probabilistic distributions.

Particular HMM structures can be designed for

speciﬁc applications. The basic left-to-right HMM

with no skip transitions illustrated in left side of Fig-

ure 2 is an example of such a speciﬁc HMM which

will be used in our motion modelling and synthe-

sis application. A left-to-right model with no skips

is a model in which the only possible state transi-

tions at each time are either to stay in the same state

or to go to the next state. The complete character-

isation of a HMM requires the speciﬁcation of the

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number of states, and the deﬁnition of two proba-

bility measures: the state transition probabilities t

i, j

between each states pair (s

, s

) and the probability

density functions (pdfs) e

of the observations in each

state s

. In continuous HMMs, these pdfs are most

often modelled by a mixture of Gaussians, or single

Gaussians as illustrated in Figure 2. Following the

approach proposed by (Yoshimura et al., 1998) for

HMM-based generation, the transition probabilities

can be replaced by Gaussian state duration pdfs d

, as

illustrated in the right side of Figure 2. The HMM is

then called a Semi Hidden Markov Model or SHMM.

A compact notation is often used to refer to all the pa-

rameters deﬁning the HMM or SHMM, and such a set

of the model parameters is written λ.

Figure 2: A simple three-states left-to-right HMM with no

skip with its associated observation pdfs. A classical HMM

is presented on the left side, while its SHMM correspon-

dence is presented on the right side.

2.3 HMM-based Motion Modelling

HTS is the framework we used for training our mod-

els and for synthesis. It was designed speciﬁcally

for speech synthesis. However very strong similar-

ities can be found in the speech and motion modal-

ities. The left-to-right structure of phonemes cor-

responds to the structure of basic motions, identity,

emotions and physical characteristics will inﬂuence

both speech and motion modalities, etc. HMM-based

motion modelling and synthesis can hence greatly

beneﬁt from tools developed primarily for speech.

In previous work (Tilmanne et al., 2012), we

transposed the HTS speech designed training and syn-

thesis procedure to our motion use case. In the present

work, walk is modelled using only two SHMMs, one

for the right step and one for the left step. We mod-

elled each step with a ﬁve-states SHMM, with mul-

tivariate diagonal Gaussian observation pdfs and uni-

variate Gaussian duration pdfs. For reasons related to

the parameter generation (see Section 3), the ﬁrst and

second derivatives of the motion data are also mod-

elled. Our walk model consists in 2 (one SHMM per

step) * 5 (states) * [ 2 (mean and variance of duration

pdfs) + 2 (mean and variance of observation pdfs) *

54 (dimensionality of mocap data) * 3 (static, ﬁrst and

second derivatives) ] = 3260 parameters.

In a ﬁrst stage, a global walk model is trained

using the complete Mockey database. For each one

of the eleven walk styles, this generic walk model

then undergoes an adaptive training with a reduced

set of data corresponding to the target style only. Af-

ter adaptation, we obtain twelve distinct stylistic walk

models: one neutral style model and one model for

each one of the eleven Mockey styles. Thanks to

the adaptive training, all models are adapted from the

same basis model and are therefore aligned.

3 GENERATION OF MOTION

PARAMETERS BASED ON

“TRAJECTORY HMMS”

Although HMMs have already been tried for char-

acter animation (Brand and Hertzmann, 2000; Li

et al., 2002; Wang et al., 2006), our prototype re-

lies on a different approach towards HMM-based pa-

rameter generation. Indeed, as described in Sec-

tion 2, the core concept behind our motion modelling

framework is the adaptation of advanced HMM-based

speech processing algorithms to the motion use case.

Over the last decade, the speech research community

has come up with very innovative solutions involv-

ing HMMs. Among these innovations, Tokuda et al.

have proposed an algorithm for the use of SHMMs

in speech synthesis (Tokuda et al., 2000). This idea

has brought a new category of statistical parametric

speech synthesizers and the HMM-based speech syn-

thesis toolkit, called HTS (Tokuda et al., 2008), has

become a reference in the ﬁeld. In Section 3.1, we

describe the algorithm that turns SHMMs into trajec-

tory HMMs, enabling the synthesis of realistic param-

eter trajectories, and we validate this approach for our

motion models. Our motion exploration application

relies on two other innovations from the HTS research

ﬁeld. The ﬁrst one turns trajectory HMMs into reac-

tive HMMs, i.e. achieves the parameter generation in

realtime, and is explained in Section 3.2. The sec-

ond one is called model interpolation and Section 3.3

shows how we use it to create a stylistic motion space.

3.1 Maximum Likelihood Parameter

Generation: Trajectory HMMs

The relevance of the HTS algorithm in synthesising

high-quality speech trajectories relies on a core algo-

ExplorationofaStylisticMotionSpaceThroughRealtimeSynthesis

805

rithm by Tokuda et al. called the Maximum Likeli-

hood Parameter Generation or MLPG (Tokuda et al.,

2000). Practically it means that we try to generate

new parameter trajectories that are the most likely to

be observed, for a given statistical model λ.

As described in Section 2, each class label of

the database actually corresponds to a N-state left-

to-right SHMM. When synthesising a new sequence,

a transcription of the sequence of class labels to be

synthesised must be available, and each class label

queries its corresponding model. In our gait mod-

elling use case, N = 5, although all the Figures of

this Section display 3 states s

for readability reasons.

For each state s

, the SHMM contains a univariate

Gaussian distribution d

as the state duration model

and a multivariate Gaussian distribution e

as the ob-

servation vector model. The process described below

works for any series of class labels (not just one), as

the SHMMs can be concatenated together before the

parameter generation. However in our walk synthesis

use case, the walk sequence will always correspond to

a succession of right and left steps, and the walk se-

quence model will hence consist in a concatenation of

ﬁve-states models corresponding alternatively to the

right and left step models.

3.1.1 “Unwrapping” the Duration Models

The ﬁrst phase in generating a parameter trajectory

from the queried SHMM is to solve the duration

model for each state. According to the Maximum

Likelihood criterion, each state s

of the SHMM must

last the most likely amount of time, i.e. the mean of

its corresponding duration probability density func-

tion d

. Therefore the so-called graphical model of

the SHMM is constructed by repeating each s

a given

amount of times, corresponding to E(d

), where E

means expectation, as described in Figure 3.

This approach advantageously replaces the typical

transition matrix of HMMs with an explicit model,

making this ﬁrst “unwrapping” phase much easier to

achieve, but also to control. Indeed the duration of

each state is very visible and accessible. For instance,

the ﬁve states used in our modelling of gait are ex-

pected to be different phases of the steps. There-

fore we could easily inﬂuence the motion stylistics by

shortening or lengthening some of the state durations.

3.1.2 Generation of Parameter Trajectories

from the Observation Models

Once we have built the graphical model from dura-

tion models, we have a left-to-right structure that sup-

ports the evolution of observation statistics over time.

Practically it means that we can associate emission

Figure 3: Illustration of the overall process of solving the

duration models contained in a given SHMM (queried by

the class label). For each state s

, the mean of each duration

pdf d

is applied on the amount of repetitions of the state, in

order to build the SHMM graphical model.

probabilities to any point on the timeline of the ex-

pected parameter trajectory. Figure 4 (top part) gives

an overview of this alignment process, as the follow-

ing step of what has been created in Figure 3.

Figure 4: Illustration of the overall process of parameter

generation from observation vector statistics, as aligned on

the graphical model. The MLPG adds operations to this

process so that it generates a smooth, most likely trajectory.

However solving this graphical model under the

strict Maximum Likelihood criterion would result in

emitting the mean of the observation vector for each

state s

. The resulting trajectory would be a step func-

tion with abrupt transitions when i is incremented, e.g.

when s

switches to s

. The MLPG algorithm solves

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806

this issue by adding two more operations:

• Before computing observation statistics, observa-

tion vectors o are extended with their ﬁrst and sec-

ond derivatives, so that O = [ o ∆o ∆∆o ]. Then the

extended observation vectors O are used instead

of o. Such HMMs extended with some of the ob-

servation derivatives are called trajectory HMMs.

• A speciﬁc matrix operation (Tokuda et al., 2000)

is achieved on the O vectors and parameters of the

statistical models λ so that the Maximum Likeli-

hood is found on the whole sequence, instead of

state by state. As this process is achieved on O

and not o, the likelihoods of the ﬁrst and second

derivatives are also optimised, resulting in smooth

o trajectories. The smoothing process is illus-

trated in Figure 5 (bottom part).

3.1.3 Realistic Motion Synthesis

As a result of applying the MLPG algorithm on mo-

tion vectors, we are able to generate very realistic mo-

tion parameters from the class labels. In our use case,

we have used either class labels for one average gait

model or differentiated class labels for each style. In

both cases, we have veriﬁed with subjective tests that

the animated characters were walking in a very natu-

ral manner (Tilmanne et al., 2012).

0 50 100 150 200

−40

−30

−20

−10

Sample number

Euler angles

One example of original walk sequence

0 50 100 150 200

−40

−30

−20

−10

Sample number

Euler angles

HTS synthesised walk sequence

Figure 5: Comparison of original gait motion trajectories

vs. the ones synthesised with the MLPG algorithm. The

plot represents the evolution of the 3D Euler angle of the

left hip during a 7.7 seconds walk sequence.

3.2 MAGE: Realtime and Reactive

HMM-based Parameter Generation

The idea of exploring is intrinsically related to the in-

teractivity of the medium. Indeed the explorative ex-

perience can only be realised if the medium – in this

case the motion stylistics – is able to accept user inter-

action, meaning that the parameter generation process

reacts accordingly. Although the MLPG algorithm is

primarily based on a “all-at-once” approach, our team

has been working for several years on a new short-

term MLPG (ST-MLPG) algorithm (Astrinaki et al.,

2012). The ST-MLPG starts to generate the trajecto-

ries in realtime, i.e. as soon as the ﬁrst class label is

parsed. We showed that this algorithm preserves the

required naturalness and smoothness of the generated

trajectories, while offering the opportunity to alter the

underlying statistics on-the-ﬂy.

The new ST-MLPG algorithm and the overall idea

of reactive HMM-based synthesis led us to release a

modiﬁed version of the HTS software, called MAGE

(Astrinaki et al., 2011). The MAGE software library

has been designed to be user-friendly and accessible

for non-experts in HMMs. However the existing ver-

sion was only available for speech synthesis. This

work on gait modelling enabled us to get MAGE to be

completely compliant with mocap data, and therefore

be the ﬁrst realtime SHMM-based motion synthesiser.

3.3 Creating a “Stylistic Space” with

Model Interpolation/Extrapolation

The ability to compute the ST-MLPG included in

MAGE on our motion models and alter the parameters

of those models – i.e. means and variances – in real-

time gives a ﬁrst reasonable glance at the exploration

of motion styles. Indeed, in our gait modelling use

case, it means that we can switch instantaneously be-

tween various styles, compare them, slow them down,

etc. However a very important feature is still miss-

ing in order to truly present a “stylistic space” to the

user: the ability to continuously travel between (inter-

polate) and beyond (extrapolate) existing styles.

This notion of model interpolation exists in the

HTS research – hence for speech modelling – for

quite some time (Zen et al., 2009). The idea is to

train K differentiated models λ

. We call λ

the aver-

age model, i.e. the model trained on all the available

data, and each k then corresponds to a new model,

trained only on the data of a given style. For instance,

in our gait modelling use case, λ

would be proud, λ

decided, etc. Then we can build a new model λ

which each parameter is the weighted sum of all the

same parameters taken in the K models λ

(or a sub-

set). Both duration and observation models are modi-

ﬁed in the computing of the parameters of λ

Depending on the strategy behind the weighted

sum, the resulting λ

model can be (see Figure 6):

• the blending between two or more arbitrary styles;

ExplorationofaStylisticMotionSpaceThroughRealtimeSynthesis

807

• the inhibition of a given style: interpolation be-

tween a style λ

and the average model λ

;

• the exaggeration of a given style: interpolation

between a style λ

and the average model λ

, ex-

trapolating beyond the style λ

;

• the inversion of a given style: interpolation be-

tween a style λ

and the average model λ

, ex-

trapolating beyond the average model λ

Figure 6: Illustration (on two parameters) of how a

weighted sum between K differentiated models λ

can help

to create inhibition, exaggeration, inversion of a given style,

and also blending between two or more arbitrary styles.

Model interpolation based on weighted sums was

already available for speech synthesis in the existing

version of MAGE (Astrinaki et al., 2012). Along with

adapting the computation of the MLPG for mocap

data, we have also adapted the model interpolation

routines. As a result, we can now load our whole

collection of HTS models trained on various motion

styles in MAGE and interactively change the weights

between the parameters of those models, so as to cre-

ate the λ

model that is required by the MLPG. This

feature ultimately creates an interactive stylistic space

based on motion statistics. Indeed the user action of

inhibiting, exaggerating, inverting a style or blending

between arbitrary styles will instantaneously be con-

verted into the ongoing animation trajectories to be

applied on the virtual character.

4 APPLICATION DESIGN

Section 3 has described the series of improvements

that we have achieved on the MAGE software library.

In this Section, we show how we have integrated this

new version of MAGE in an end-user application for

the exploration of motion stylistics. In the design of

this application, we wanted the user to be able to care-

fully mix styles, like a musician mixes sounds from

different tracks. Therefore we have developed a stan-

dalone “console-like” application that sits on the top

of Blender (Blender, 2002) and provides a layer of

stylistic abstraction to the user, while sending anima-

tion trajectories in realtime to the virtual character.

Figure 7: Summary of the design used to create our stylistic

gait exploration application: Blender-rendered virtual char-

acter receiving animation data and a standalone GUI run-

ning MAGE in order to send the animation data.

In term of implementation, the application relies

on an OSC communication running between Blender

and a custom standalone C++ application for the

model interpolation and the parameter generation al-

gorithms as included in MAGE. A Python script has

been developed in Blender so as to listen to incoming

OSC messages and apply the received animation data

to the virtual character in realtime. On the other side,

openFrameworks has been used as our C++ applica-

tion builder. MAGE has been integrated in a single-

window openFrameworks project with a series of cus-

tom sliders directly connected to the weights applied

on each λ

. As described in Figure 7, each model

slider could either inhibit, exaggerate or invert the

model within the overall blending strategy. An illus-

tration of reactive walk style control can be found at

http://youtu.be/OeUmVDxdJc8.

5 DISCUSSION

The application described in Section 4 has been infor-

mally presented to a group of test users. As soon as

the application is started, the virtual character walks

continuously. At the starting point, the character

walks in a neutral way, i.e. with no speciﬁc inﬂu-

ence of any λ

stylistic model on the average model.

Users were proposed to play freely with the console

of sliders and highlight the interesting walking styles

that they would ﬁnd through the exploration.

As illustrated in Figure 8, the user exploration

reveals a much wider range of stylistic expression

than any top-down observation of original mocap data

would have suggested. By playing with features like

inhibition, exaggeration and inversion of a given style,

the users get a much more impersonated perspec-

tive on what has been originally recorded and statisti-

cally modelled for that style. By combining arbitrary

styles, users create new walks that, if they have not

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808

Figure 8: Examples of gait poses coming from the ex-

ploration application for various conﬁgurations of the slid-

ers. We can see inhibited/exaggerated versions and arbitrary

combinations of various styles.

been originally recorded, are however consistent with

the overall stylistic behaviour of the captured subject.

With this application, users have been able to get

their hands on a very high-dimension space (orig-

inally 3260 parameters) through a simpliﬁed GUI,

while not reducing nor hiding its complexity and vari-

ability. Compared to playing back original mocap se-

quences, the ability to browse a continuous stylistic

space in realtime is more interactive and user-centred.

Finally the representation of motion data through a

3D virtual character helps users to experience the

real motion and not a non-intuitive series of motion

curves. We think this made a huge difference in users’

ability to understand the ongoing stylistics.

6 CONCLUSIONS

In this paper we presented an innovative approach to

the exploration of stylistic motion capture databases

through a realtime motion synthesis framework. The

feasibility and pertinence of this approach has been

demonstrated on an expressive gait space exploration

use-case. Our application enables the user to freely

browse the stylistic space, exaggerate, inhibit or in-

vert the styles present in the training data, but also

to create new styles through combination of the exist-

ing styles. This reactive control provides a completely

new way of visualising and exploring the motion style

space. Since motion style is a notion difﬁcult to de-

scribe or apprehend, we believe this approach to be a

valuable tool for the exploration and comprehension

of expert gestures which are a part of the intangible

cultural heritage which is very difﬁcult to represent.

ACKNOWLEDGEMENTS

J. Tilmanne and T. Ravet are supported by the

European Union (FP7-ICT-2011-9), under grant

agreement n

600676 (i-Treasures project). N.

d’Alessandro is funded by a regional fund called

egion Wallonne FIRST Spin-Off. M. Astrinaki is

supported by a PhD grant funded by UMONS and

Acapela Group.

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