Adding Cartoon-like Motion to Realistic Animations

Rufino R. Ansara and Chris Joslin

School of Information Technology, Carleton University, 1125 Colonel By, Ottawa, Canada

Keywords: Computer Graphics, Cartoon, Motion Capture, Three-dimensional Graphics and Realism, Animation.

Abstract: In comparison to traditional animation techniques, motion capture allows animators to obtain a large amount

of realistic data in little time. In contrast, classical animation requires a significant amount of manual labour.

The motivation behind our research is to look at methods that can fill the gap that separates realistic motion

from cartoon animation. With this knowledge, classical animators could produce animated movies and non-

realistic video games in a shorter amount of time. To add cartoon-like qualities to realistic animations, we

suggest an algorithm that changes the animation curves of motion capture data by modifying their local

minima and maxima. We also propose a curve-based interface that allows users to quickly edit and visualize

the changes applied to the animation. Through our user studies, we determine that the proposed curve

interface is a good method of interaction. However, we find that in certain cases (both user-related and

algorithmic), our animation results exhibit unwanted artefacts. Thus, we present various ways to reduce,

avoid or eliminate these issues.

1 INTRODUCTION

Since the release of Toy Story, the first full-length

computer-animated cartoon movie, cartoon

animation has slowly transitioned from 2D to 3D

animation methods. Studios like Pixar and

DreamWorks use manually created character rigs,

inverse kinematics and keyframe based animation to

bring their characters to life – no motion capture is

used. While these methods give the animator full

control over the final animation, they also involve

significant manual labour as animators must

manipulate all the character’s limbs independently.

Motion capture is significantly less time

consuming than classical animation; although there

might be more setup time required for the real-

environment and post-processing of the animation

itself; its emergence has revolutionized computer

animation. This success is apparent in the

dominance of high quality, realistic, animations used

in movies such as Lord of the Rings, Avatar, and

Planet of the Apes, and in games such as Final

Fantasy, Grand Theft Auto, Call of Duty, and Halo.

However, despite the quality of the animations

produced, motion capture is seldom, if ever, used by

3D animation studios for non-realistic characters.

These studios adhere to the principles of animation

originally defined by Disney that are now considered

an industry standard (Thomas and Johnston, 1995).

Many of these principles are elements of movement

that are not found in real life and thus, motion

capture alone cannot be used to acquire these

movements. The motivation behind our research is

to look at how to fill the gap that separates realistic

motion from cartoon animation. We want to simplify

the animator’s work without completely sacrificing

their ability to manipulate the result. Thus, our end

goal is to allow people to use motion capture data to

output cartoon animation.

To speed up the editing process, we propose an

algorithm that changes the animation curves by

modifying the local peaks and troughs. We focus on

exaggerating angular and positional motion to

produce more animated results and explore the

ability to change an animation’s style. We also

present a simple curve-based user interface that

allows users to visualize potential changes and apply

them to the animation. Finally, we present the results

of our usability tests on the algorithm, the interface,

and the quality of the resulting animations.

2 RELATED WORKS

The ever-growing demand of 3D animation has led

researchers to look for innovative ways to either

synthesize or modify already existing 3D human

motion. The following section describes work that

Ansara R. and Joslin C.

Adding Cartoon-like Motion to Realistic Animations.

DOI: 10.5220/0006174001370147

In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), pages 137-147

ISBN: 978-989-758-224-0

137

accomplishes this through three different methodolo-

gies: We briefly cover space-time constraints and

machine learning and focus on motion warping

methods, as it is closest to our methodology. Finally,

we provide a synopsis of the state of human

computer interaction within this context.

2.1 Spacetime Contraints

Spacetime constraints (Witkin and Kass, 1988)

defines motion within optimized constraints in direct

relationship to the entirety of the action (and is not

constrained per keyframe). In this first research

paper on the topic, Witkin and Kass successfully

allowed Luxo Jr., the lamp mascot from Pixar, to

perform multiple different types of jumps based on

these constraints. Similarly, Popovic and Witkin

attempt to implement dynamics to spacetime

constraints with good visual results (Popović and

Witkin, 1999). In extension, Li presents a method

that allows an animator to edit an already existing

animation while preserving the inherent quality of

the original motion (Li, Gleicher, Xu, and Shum,

2003) . Comparable research includes the generation

of transitions between animations (Rose, Guenter,

Bodenheimer, and Cohen, 1996) with the goal of

blending different snippets of realistic humanoid

animation seamlessly, the creation of a method for

the rapid prototyping of realistic character animation

(Liu and Popović, 2002) and others.

As mentioned in several of the papers, the main

limitation when it comes to animating with

spacetime constraints is the need for free-form

animations that fall outside pre-determined

constraints. These types of animation are difficult to

achieve without substantial user intervention.

2.2 Machine Learning

Machine learning uses concepts found in nature to

give computer systems the ability to learn and adapt

themselves to different situations. The following

section presents papers that use various methods of

machine learning to teach computers the different

aspects of human motion and use that pre-gathered

information to generate new, user-defined

animations.

Brand and Hertzmann present Style Machines, a

system that permits the stylistic synthesis of

animation (Brand and Hertzmann, 2000). The focal

point of their research is the development of a

statistical model that allows a user to generate an

array of different animations through the use of

various control knobs. Kuznetsova et al. present an

innovative technique where the mesh and the motion

are combined, synthesizing both human appearance

and human motion (Kuznetsova, Troje, and

Rosenhahn, n.d.). Furthermore, in Motion Synthesis

from Annotations (Arikan, Forsyth, and O’Brien,

2003), Arikan et al. combine constraint-based

animations with data-driven methods to present a

framework that uses a motion database to generate

user-specified animations.

Other work in motion synthesis and editing

through machine learning includes the production of

poses through a set of constraints and a probability

distribution over the space of possible poses

(Grochow and Martin, 2004), the classification of

stylistic qualities from motion capture data by

training a group of radial basis functions (RBF)

neural networks (Etemad and Arya, 2014), the

combination of IK solvers and a database of motion

to create human manipulation tasks (pick-up, place),

(Yamane, Kuffner, and Hodgins, 2004) the use of

parametric synthesis and parametric motion graphs

to produce realistic character animation (Heck and

Gleicher, 2007), and the generation of two-character

martial art animations (T. K. T. Kwon, Cho, Park,

and Shin, 2008).

The main drawback behind machine learning lies

in the need to populate the motion database with

many different clips to allow for varied results.

2.3 Motion Warping

Motion warping consists of taking a pre-existing

motion curve and editing it to get an array of

different derivative animations from one source

motion. In the following section, we focus on

warping systems whose direct goal is to add cartoon

effects to realistic motion.

In motion signal processing (Bruderlin and

Williams, 1995), the authors introduce techniques

from the image and signal processing field for

motion editing. They collect all motion parameters

from the animation curve of a pre-determined

keyframe and convert this into a signal. The large

majority of the research this paper discusses focuses

on the joint and angle positions of the human

hierarchical representation. In their work, Bruderlin

and Williams apply signal-processing techniques

and modify an animation’s trajectory or speed. They

apply a multiresolution filtering on a walk animation

by increasing the different sub-sections of the

different frequencies collected from the original

motion signal. Their results show that when they

increase the middle frequencies, they obtain a walk-

cycle that’s both smooth and exaggerated. However,

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when they choose to only increase the high

frequencies, they cause an unnecessary twitching of

the character. Although they do not explain the

reason, we believe that by increasing those high

frequencies, they are dramatizing the effect of the

noise in the original data.

Wang et al. presented a method called “The

Cartoon Animation Filter” that takes arbitrary input

motion and outputs a more animated version of it

(Wang and Drucker, 2006). By subtracting a

smoothed version of the second derivative of the

original curve (of the animation) to the original

curve itself, they can add anticipation/follow-

through and squash and stretch to a variety of input

motions. Their method is the equivalent of applying

the inverted Laplacian of a Gaussian filter to the

animation curve. In their conclusion, they

acknowledge that the resulting animations do not

satisfy the expected quality of hand-crafted

animations and are more suited for quick, preview-

quality animations.

White et al. built on Wang’s work by creating a

slow in, slow out filter that, when used correctly, can

“add energy and spirit” to an animation (White,

Loken, and van de Panne, 2006). They accomplish

this by identifying eligible keyframes (through

Kinematic Centroid Segmentation (Jenkins and

Mataric, 2004)) and then applying a time-warp

function to obtain the desired result. They

demonstrated the effectiveness of their algorithm

with a few motion-capture animations, but their

paper shows little description of their final result.

Kim et al. (Kim, Choi, Shin, Lee, and Kuijk,

2006) attempted to generate cartoon-like anticipation

effects by reversing joint rotation and joint position

values. Their results show that their anticipation

generation produces more expressive animations and

that it’s an efficient tool for amateur animators.

However, the main drawback of their system is that

it heavily relies on user input to define the

anticipation and the action stage, a task that proved

to be time consuming for some users. Finally,

Savoye presented a method that uses Wang et al.’s

cartoon filter to allow an animator to modify a pre-

existing realistic animation with a single editing

point (Savoye, 2011). For example, a user can move

an ankle and Savoye’s algorithm can be used to

estimate the global position of all the other joints in

the skeletal hierarchy. He achieves this by

minimizing the sum of the squared difference

between the original motion capture data and the

animator-edited animation data. However, Savoye’s

results suffer from the same issues as the cartoon

animation filter. While the resulting animations do

provide adequate cartoon characteristics, the overall

quality is lacklustre when compared to keyframed

animation and is not recommended as a complete

replacement to manual key-framing.

2.4 Novel Animation Systems and User

Interaction

While a system’s algorithm is a key influence for the

quality of the resulting animations, human factor is

equally important. The system by Goodwin provides

excellent results, but can be difficult to use despite

the quality of the animation (Goodwin, 1987);

therefore, it can still considered unsuccessful from a

practical point of view. In the following section, we

will present a short overview of the state of usability

considerations in motion synthesis and editing.

Many of the research projects presented above

focus on the system functionality and performance

without any mention of the human factor involved

(J. Kwon and Lee, 2008; Witkin and Popovic, 1995).

For example, the cartoon animation filter (Wang and

Drucker, 2006) does not describe any method for

user interaction. Other work, such as Space-time

constraints (Witkin and Kass, 1988), Synthesis of

complex dynamic character motion from simple

animations (Liu and Popović, 2002), Style Machines

(Brand and Hertzmann, 2000), and Synthesis and

editing of personalized stylistic human motion (Min,

Liu, and Chai, 2010) consider usability issues, but

do not formally test for them. There’s a considerable

amount of interesting interaction systems that have

been developed but do not formally conduct user

studies. For instance, the “Style Machines” (Brand

and Hertzmann, 2000) system uses a series of

control knobs to manipulate the difference stylistic

changes to the animation and Rose et al.’s method

allows users to generate motion transitions through

their own custom motion expression language (Rose

et al., 1996).

Despite the strong interaction models described

in these related works, the main drawback behind

these systems is the lack of real user data to support

the claims that they’re intuitive and easy to use.

3 METHODS

3.1 Motion Capture

To track human motion, we attached reflective

markers to participants’ bodies by having them wear

a tight, black Velcro suit that acts as a membrane

Adding Cartoon-like Motion to Realistic Animations

139

and prevents unnecessary reflections from the skin.

We recorded all marker data at 120fps using an

optical motion capture system from Vicon with six

MX40 and four T20 cameras. We used Vicon Blade

1.7.2 for setup, control, and recording; essentially to

process the data and output skeletal joint rotations as

Euler angles.

3.2 Data Collection

To provide a large variety of input data for our

system, we collected several animation clips from a

total of 15 participants (8 male, and 7 female) in

motion capture sessions that lasted up to 2 hours.

We focused on three types of motions: exaggerated

joint rotations, unrealistic jumps, and stylistic walk

cycles.

Due to the nature of our research goals, our

motion captured animations were inspired from a

collection of different animated movies. As they are

re-enacted by real human beings with real human-

proportions, we did not expect the motion capture

recordings to be 100% faithful to the animated

movie clip. Instead, we used them as a base for

interesting recordings that can be both re-enacted by

motion capture actors and edited by animators.

Because of these constraints, we chose snippets of

animations from movies that had a large variety of

bipedal characters, such as Toy Story, Rise of the

Guardians, Frozen, Despicable Me, The Croods and

Hotel Transylvania.

3.3 Plugin Development

We used Autodesk Maya’s IDE and Python 2.7 to

develop a plugin that allows a user to edit animation

data from an imported Vicon Blade skeleton (in

FilmBox, FBX, format) directly within Maya.

3.3.1 Exaggeration

Our method is reminiscent of the cartoon animation

filter (Wang and Drucker, 2006), which exaggerates

the peaks and troughs of a curve to add cartoon-like

motion to animation data. However, instead of using

a Laplacian Gaussian curve function to dramatize

the peaks, we define an exaggerated point (x’) as the

addition (when greater than the median) or

subtraction (when smaller than the median) of a user

specified coefficient C. This coefficient is scaled to

take the relationship between the original point x,

the median (m) and the global maximum or

minimum values of the overall animation (M). A

visual representation of this method is shown in

Figure 1.

x’ = x ± (C * ((x - m) / (M - m))) (1)

To reduce the processing power required, we

only apply the exaggeration formula above to the

pre-determined points and use cubic interpolation to

determine the points in between. Figure 2 shows a

visual overview of the process.

Figure 1: Maximum and Minimum Manipulation.

Figure 2: Algorithm Flowchart.

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3.3.2 Jump Exaggeration

Our jump trajectory exaggeration feature is an

extension to the general exaggeration method. The

main difference is that instead of exaggerating the

rotation values of all the joints, we specifically target

the Y translation value of the root joint and apply

our algorithm on that specific axis. This results in

jumps with higher amplitude and an added bouncing

motion to the landing sequence. Furthermore, to

avoid an over-simplification of the jump trajectory,

we maintain more than simply the

maximum/minimum keyframes.

3.3.3 Exaggerated Feminine Walk

We use the minima and maxima modification

algorithm to modify a neutral walk into an

exaggerated stylized female walk (exaggerated hip

and spine motion) akin to characters such as Jessica

Rabbit, Elsa from Frozen, etc. However, instead of

increasing the maximum and minimum values

uniformly, we target the main features of the walk

and modify them accordingly.

In order to determine how to change a neutral

walk into a feminine-stylized walk, we collected

samples of a neutral walk and of exaggerated female

walks. To compare them accurately, we determined

the beginning of each walk cycle by looking at the Z

position of the foot joint. With the cycles lined up,

we examined the difference in amplitudes between

our neutral walks versus our exaggerated walks.

Using this, we determined the coefficients needed to

modify the animation. By adding scaled versions of

these values to the stylized walk, we approximate

the actual rotation values of an exaggerated feminine

walk.

Exaggerating the hip and root joint rotations

alone caused a significant amount of feet sliding

(more detail in Section 3.3). Consequently, we opted

to anchor the feet with IK handles and bring them

closer together to give the illusion of an exaggerated

hip motion. While this significantly reduces the

direct rotational exaggeration, it produces a more

feminine walk while removing the undesirable foot

sliding artefact.

3.4 Curve Interface

We created a curve-based interface to provide users

an easy and straightforward way to edit motion

capture clips while showing a clear visual metaphor

for the changes that will be applied on the

animation. Users manipulate the curve by translating

(using the default Maya interface tools) the arrow

controls. These shapes are constrained to deformers

that move the curve’s control points. This effectively

morphs the curve depending on how the user

manipulates it. This is a simplified version of how

animators currently edit animation curves. However,

instead of potentially hundreds of control points (x,

y, z coordinates, keyframes, etc.), we reduce the

interactions to three arrow controls. Figure 3 shows

two curves: the first curve, in grey, denotes the

default position of the curve. The red curve shows

an attempted user edit. The X axis denotes time and

the Y axis denotes “amount of change”.

Figure 3: Input curve (larger amplitude, red) in

comparison with the default curve position (smaller

amplitude, gray).

If users try to apply the default curve to the

animation, there is almost no change. In such case,

the system finds the local maximum and minimum

and re-creates the curves using cubic interpolation,

but without changing the peaks or troughs.

Conversely, any change in the Y axis will increase

the value of local maximums, decrease the value of

local minimums and interpolate a new curve based

on these new points. The amount of change applied

to the local extremes is a scale extracted from the

difference between the original curve and the edited

curve. Similarly, any change in the X axis applies a

uniform time-scale based on the root rotations to

emphasize the extreme values of the animation.

Figure 4: System Overview.

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141

The complete system interface is illustrated in

Figure 4. The curve based interface is on the left side

of the window, the animation preview window on

the centre and the dialog window (on the right side)

is mobile and can be moved around to a user’s

preference.

3.5 Foot Sliding

A ubiquitous problem with editing human motion

through joint rotation values is that, because all the

joints are organized in a hierarchy, exaggerating the

rotation values of the parent joints can cause

unexpected results further down. Thus, each

exaggeration that is applied creates a successively

increasing amount of motion in the end-position of

the last joint. While this doesn’t affect the upper

body negatively, it causes something called foot

sliding or foot skating in the lower body, and is

generally a problem that still hasn’t been completely

solved (Kovar, Schreiner, and Gleicher, 2002). As

this affects the visual quality of the animations

significantly, we implemented a method to correct

this issue using inverse kinematic handles to anchor

the feet on the floor as necessary. To determine

when the foot is bearing weight (and thus,

immobile), we find the local minimums of the Y

position of the ankle and key-frame the IK handles

to those positions. This process prevents the feet

sliding effect from happening. While it effectively

reduces the overall exaggeration of the lower body,

it allows us to avoid awkward animations where

character’s legs seem to be sliding erratically on the

floor.

4 USER STUDIES

4.1 Part A – User Editing

In Part A of our user studies, we asked participants

to edit a random selection of ten motion capture

animation clips we recorded in our data collection

phase. This includes one practice run and three

animations per category. All outputted animations

were saved along with the curve used to edit them,

as well as a reference to their original clip. Our goals

were as follows:

 Determine the overall usability of our animation

editing system. Will people understand how to

edit the animations? Will they grasp the

dimensionality of the curve control? Will they

find it easy or frustrating?

 Evaluate the visual results produced by the users

and verify whether the curve input illustrates the

changes in motion effectively. How do they feel

about the original animation in comparison with

the edited animation? Do they understand the

motion change? Which do they prefer? Do they

see the relationship between the edited animation

and inputted curve?

We recorded user feedback by asking a series of

questions related to the animation quality and

interactions with the system. The participants filled

out a demographics questionnaire before the study

began, an animation data sheet after each edited clip,

and a final questionnaire at the end of the study to

collect their overall opinion of the session.

4.2 Part B – Evaluation

In this second user study, we had a complete

different set of participants watch fifteen animations

that were created by participants in part A (three of

each category) and collected their opinions through

Likert-scale questions as well short and long form

questions.

Similar to our previous user study, our goals were as

follows:

 Compare and evaluate the animation quality by

users who were not involved in the creation

process and have no emotional attachment to

the final product.

 Evaluate the user input as well as the relation of

the curve to the animation change.

The participants filled out a demographics

questionnaire before the study began, an animation

data sheet after each clip they watched, and a final

questionnaire at the end of the study to collect their

overall opinion of the session.

5 RESULTS

5.1 Algorithm Results

The following chapter shows examples of how our

algorithm changes all three animation categories. A

quantitative analysis of the participant user

experience and rating of the animations is detailed in

section 5.2.

5.1.1 Joint Rotation Exaggeration

Our method of exaggerating animation curves is as

discussed in Section 3.2 This method is effective at

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intensifying the maximum and minimum rotational

values of the character’s joints (namely the root,

spine, shoulders, arms, hips, ankles) without over-

dramatizing smaller motions. During Part A of our

user study, participants exaggerated the overall

rotation of a total of 48 dances, walks or runs

selected in a randomized order.

5.1.2 Jump Trajectory Exaggeration

As can be seen in Figure 5, our method of jump

trajectory modification effectively increases the

jump height and the anticipatory motion in an

aesthetically pleasing fashion. We asked participants

to exaggerate the jump trajectory of three clips,

chosen randomly from 48 different animation

sources.

Figure 5: Original (blue) and Edited (red) Jumps.

5.1.3 Neutral Walk to Stylized Walk

(Feminine)

Our final application of our algorithm is the stylistic

change between a neutral, gender-ambiguous walk

to a stylized cartoon female walk. Figure 6 shows an

example of an animation resulting from the

algorithm as applied to a neutral walk. The edited

animation has more dramatic spine movements and

reduced arm/shoulder movements when compared to

the original animation. The hip motion is

constrained because of the foot slide issue, as

described in Section 3, which led us to anchor the

character’s feet. This, in turn, restricted the rotation

of the root and hip joints. To create an exaggerated

hip motion, we chose to bring the feet closer

together. The results of our user study (Section 5.2)

show that this gave participants the illusion of more

feminine walk.

Figure 6: Neutral (blue) and Stylized Walk (red).

5.1.4 Animation Time Shifts

To emphasize the extreme motions of the

animations, we allowed users to slow-down the

animations elements close to the local minimum and

maximums. As previously mentioned, this is

achieved by manipulating the X axis in our curve

interface. To maintain the homogeneity of the

algorithm, we use the root joint as the reference joint

to determine when to slowdown the animation. We

chose this specific joint for its relative position (at

the character’s centre of gravity) and for its global

influence.

5.2 User Study Results

5.2.1 Part A – Animation Editing with the

Curve Interface

In this section, we present the results of the

questionnaire administered during Part A of our user

studies. We also provide an in-depth statistical

analysis of the questionnaire responses. In total, we

conducted sixteen one-hour user studies with 16

participants (14 male, 2 female). Each individual

edited nine animations (three walks, three jumps,

and three miscellaneous clips, chosen at random).

The resulting edited animations were used in Part B

of the user study.

Participants included undergraduate and

graduate students, as well as a few staff. A large

majority of our users were between 18 and 34 years

old, with only 4 participants over age 35. A majority

of participants, 56%, had not done any animation

before participating in our study. The remaining

participants had experimented with animation in

undergraduate courses or through self-learning of

various animation methods, such as cell-shading

animation, motion capture with Microsoft’s Kinect

or video editing. Twelve of our sixteen participants

(75%) rated their familiarity with Autodesk Maya as

very low. In our post-study questionnaire, we

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143

evaluated the overall satisfaction level of

participants in terms of functionality, interactivity

and learning curve of our animation editing system.

The results shown in Figure 7 demonstrate that over

80% of participants had a high or very high

satisfaction level in all three of our usability criteria.

Figure 7: User Satisfaction with the Functionality,

Interactivity and Learning Curve of our animation editor.

To further evaluate the usability of our system,

we also compared the overall difficulty of our four

interaction tasks with the difficulty of three of

Autodesk Maya’s basic functions:

 Transformations, such as object translation,

rotation, and scaling (Move the character)

 Camera manipulations, such as pan, skew, yaw

(Move the camera),

 Animation controls, such as play animation,

stop animation, change start/end keyframe

(Manipulate the timeline)

To do this, we compared participants’ average

rating of our interaction tasks with their average

rating of the Autodesk Maya tasks. Both the average

ratings of our interaction tasks, D(16) = 0.197, p =

.097, and the average ratings of the Autodesk Maya

tasks, D(16) = 0.313, p = .000, are significantly

different from a normal distribution, as shown by the

K-S test. Thus, we compare the two averages using

the non-parametric Wilcoxon test. 11 participants

rated our interaction tasks as more difficult than the

Autodesk Maya tasks, 4 participants rated the

Autodesk Maya tasks as more difficult, and 1

participant found them to be equally difficult.

However, no statistical difference was found

between the average difficulty rating of the basic

Autodesk Maya tasks and our animation editing

tasks, Z = - 1.936, p = .053. This suggests that our

animation tasks are equal in difficulty to basic tasks

in Autodesk Maya. However, 75% of our users had

no previous experience using Autodesk Maya and

yet the average difficulty rating for our interaction

tasks was 1.17 (0 -4 scale), which is quite low. This

leads us to believe that our curve interface is simple

and amateur-friendly, especially compared to the

more traditional animation methods discussed in our

introduction.

From our observations during the user study,

participants found the jump editing to be the most

intuitive task, as the arc of the jump visually

resembled the amplitude change in the user input.

The time-shift task proved slightly more difficult

because our implementation didn’t allow users fine

control over what parts of the animation were

slowed down.

Figure 8 shows overall participant satisfaction

with the animation quality and with our curve-based

interface. 81% (13/16) of participants agreed to

some extent that the animations they created were

visually pleasing, 75% agreed that the edited

versions were more interesting than the original

animation and 56% thought the curve was a good

visual illustration of the changes done to the

animation. It is also interesting to note that none of

the participants disagreed with the statement

“Overall, the resulting animations I created were

more interesting than the original motion”. This

suggests that our system provides both high

functionality and high usability.

Figure 8: User Satisfaction with the System Functionality,

Interactivity and Learning Curve.

5.2.2 Part B – Animation Review

In Part B of our user study, we conducted one-hour

sessions with a set of 15 new participants (9 male

and 6 female). None of the participants from Part A

participated in Part B of the user study. In each

session, the participant watched fifteen animations

(five jumps, five exaggerations, five walks) and

completed a questionnaire, giving us a total of 225

data points. These animations were randomized from

the pool of animations generated during Part A of

our user study. Of these participants, 50% had

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experience with basic animation, mostly in Adobe

Flash. Only one participant had any motion capture

editing experience.

As in Part A of the study, participants in Part B

were asked to evaluate how well the edited curve

input illustrated the changes made to the animation.

Part B participants agreed to some extent that the

curve input was a good illustration of changes made

to the animation in 63% of the exaggerated clips,

66% of feminized walks and 95% of the jumps.

These results lead us to conclude that using a simple

curve to illustrate animation changes was a success,

particularly when it comes to our jump animations.

The jump trajectory exaggeration matches directly

with the amplitude increase, likely leading to a

strong affordance between the curve interface and

the jump animation.

Despite these results, user preferences for the

edited and original animations are less promising.

Participants indicated that they preferred only 37%

of the edited exaggerated animations over the

corresponding original clips, 34% of the feminine-

stylized walks, and 59% of the edited jumps. All

three animation types had lower preferences for the

edited animations in this part of the study than in

Part A. We explored the reasons for these results by

examining the answers to the short and long answer

questions from the Part B questionnaire. Participants

who liked the animations explained that they found

the edited animations “funny”, “entertaining”, and

“unattainable through real motion”. Some comments

focused on how they preferred animations with less

extreme animation edits. Conversely, participants

who disliked the edited animations stated that they

were “over exaggerated”, “unpolished and jerky” or

“unrealistic”. These results are in line with previous

findings: too much exaggeration can cause

unappealing results (Reitsma and Pollard, 2003).

This over exaggeration is mostly the result of

purposeful user input. The lack of realism was the

direct object of the interface and the study overall.

We believe that perhaps the unrealistic motion

would be more appealing to users if the character in

the animation was an identifiable cartoon, rather

than a bare skeleton; however, this must be left for

future work to determine.

5.2.3 Evaluating Curve Input

These results led us to examine the correlation

between the participants’ preferences for the original

vs edited animations with their agreement that

sensible curve input was used to change the

animations.

By the results of the K-S test, the user opinion of

the curve input, regardless of animation type or

original vs edited animation, was found to be

significantly different from a normal distribution,

D(225) = 0.232, p = .000. Thus the non-parametric

Spearman’s rank correlation coefficient can be used

to correlate this data. 15 participants rated a total of

225 animations, and a significant correlation was

found between their agreement that the curve input

was sensible and whether they preferred the original

over the edited animation, rS = .336, p = .000. This

correlation indicates that users (in part B) who

preferred the original animation were more likely to

disapprove of the curve input used by a previous

participant (in part A) to edit that animation.

When examining the short and long answer

results, we determined a few cases where the

animation algorithm didn’t work as intended. Most

participants described the result of the feminized

walk algorithm as feminine, female, or sassy,

however a subset found it to be more reminiscent of

a drunken person walking, or someone trying to

walk on a tight-rope. These impressions can be

explained by an over-exaggeration of the spine joint,

which makes the character appear unstable. This was

then exacerbated by the over-tightening of the feet

position described previously. For the two other

categories, most criticisms came from frame-skips in

the jump animations, and the aggressive use of the

time-shift function. Despite this, considering the

skill level of participants in Part A of the study, the

average preference results are acceptable (43%).

Furthermore, we successfully show that the curve

interface is a good interaction method that’s intuitive

and easy to use for amateur users.

6 CONCLUSION

6.1 Findings

In this paper, we presented an animation editing

algorithm coupled with a new curve-based interface

with the goal of adding cartoon-like qualities to

realistic motion. The animation algorithm was based

around the idea of interpolating between modified

local minimum and maximum values. Our curve

interface provides a 2D metaphor to the animation

modification process.

The algorithm proved efficient at reducing the

realism of the motion across all three animation

types, and users found the curve interface easy to

use and understand.

Adding Cartoon-like Motion to Realistic Animations

145

6.2 Limitations

Despite the success, our work has a few limitations:

Our algorithm does not function very well at

extreme data points (large slowdowns, large

exaggerations) and can produce very unappealing

results when the coefficients are too large (>300%

change). In terms of the animation time-shift, our

method only allows for the systematic slow-down of

the randomly selected clips. To allow for more

functionality and a better metaphor between our user

interface (the curve) and the results, the ability to

speed-up the animation is lacking.

For various reasons discussed in Section 5, our

edited animations were not always preferred to the

original animations. These results can be explained

by a combination of: extreme coefficients applied

through the algorithm, user inexperience (at

animation) and a lack of context (human skeleton

instead of a cartoon character, blank setting instead

of a cartoon environment). Furthermore, our user

studies, particularly for Part A, pose a few issues.

The question “Which animation do you prefer” is

vague and subjective and thus gives us scattered

results. We suggest changing this to “Which

animation is more suitable for cartoon movies?” or

another similar question that connects more

appropriately to our study goals. In terms of the

participant pool, a better balance between genders (a

minimal ratio of 40% - 60%) would reduce bias,

particularly when it comes to the visual appeal of the

exaggerated feminine walk. Finally, to better tie in

with our system goals, the participants should have

been 3D animators, or at least have had some

experience with current cartoon animation methods.

6.3 Future Work

As future work, we’d like to point the research

topics in this direction:

Real-time editing: To make the editing process

more streamlined, we suggest the implementation of

the curve editing system in real- time. This would

allow users to make more “on the fly” editing

changes and fine tune the results.

Less restrictive foot constraints: Our foot

constraints reduced the amount of exaggeration in

joints below the hip. While this was necessary to

maintain an appropriate level of animation quality,

we suggest exploring ways to couple the feet

planting process with the exaggeration algorithm to

allow interesting modifications of the lower body

animations.

Use of cartoon-like character models and

settings when editing motion: The usage of

humanoid skeletons with realistic proportions poses

a few cognitive issues, as certain cartoon motions

can look awkward when applied to a realistic human

skeleton. This might not be because the animation

itself is inherently bad, but rather because it looked

out of place. We suggest further research to skin a

cartoon-like character to the skeleton to further

explore this issue.

ACKNOWLEDGEMENTS

We’d like to thank the School of Information

Technology at Carleton University for providing

access to their motion capture studio, as well as all

the participants who took the time to help us in our

data-collection phases and in our user-studies.

This project was funded by The Interactive and

Multi-Modal Experience Research Syndicate

(IMMERSe).

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