Evaluating the Usability of Recent Consumer-grade 3D Input Devices

C. Siegl, J. S

ußmuth, F. Bauer and M. Stamminger

Computer Graphics Group, FAU Erlangen-Nuremberg, Erlangen, Germany

Keywords:

HCI, User Interface, Modeling, Interaction, 3D.

Abstract:

Recently, 3D input devices such as the Microsoft Kinect sensor or the Leap Motion controller became in-

creasingly popular - the later specialized in recognizing hand-gestures, advertising a very precise localization

of tools and ﬁngers. Such devices promise to enable a touchless interaction with the computer in three-

dimensional space enabling the design of entirely new user interfaces for natural 3D-modeling. However,

while implementing a modeling application we found that there are still fundamental problems that can not

easily be solved: The lack of a precise and atomic gesture for enabling and disabling interaction (clicking ges-

ture) and a poor human depth perception and localization within an invisible coordinate frame. In this paper,

we show why the precision of the interaction is not limited by hardware but software constraints.

1 INTRODUCTION

Commercial 3D modeling software is traditionally

operated using a 2D input device (e.g., a mouse or

a graphics tablet) and a keyboard. The lack of af-

fordable 3D input devices in the past has forced de-

velopers of such software to implement modeling

paradigms that reduce the dimensionality of the user

interaction. In most tools, manipulations are limited

to a 2D plane, which may, for example, be perpendic-

ular to the current viewing direction or conform with

the tangent plane of the manipulated object. Some

modeling tools (e.g., Blender) go even further and

separate 3D movement into three distinct 1D move-

ments along the x-, y- and z-axis. In practice, these

concepts are usually different from software to soft-

ware and often counter-intuitive, which makes tradi-

tional 3D modeling tools hard to learn and difﬁcult to

use for non-expert users.

With the recent advance of 3D motion tracking

devices like the Microsoft Kinect

or the Leap Mo-

tion controller, input devices for 3D interaction be-

came affordable for end users. This poses an entire

new challenge for software developers to devise new

paradigms for 3D modeling and scene manipulation.

Future modeling tools will have to provide an intu-

itive and easy to use 3D gesture based user interface

that supports artists in their everyday work.

While reviewing existing applications we found

that most of them claim to make use of a 3D free-

hand gesture based input device. However, in prac-

tice they do not really use all three dimensions. Often

the interface resembles a multitouch interface without

Figure 1: Three devices for 3D interaction. We concentrate

on the Leap Motion controller (middle).

touching anything. The third dimension is solely used

as an activation or clicking gesture.

After realizing that there is no modeling soft-

ware using true three-dimensional input, we wanted

to leverage the new Leap Motion controller with its

precise spatial hand detection algorithms to imple-

ment a simple to use software for common modeling

tasks. While at ﬁrst glance, this seems to be a trivial

task, we found that there are fundamental obstacles

preventing an intuitive and precise interaction. Since

this is a problem concerning almost every software

that relies on precise spatial three-dimensional inter-

action we decided to investigate this issue.

1.1 Consumer Hardware

There are multiple consumer grade input devices with

variable technical speciﬁcations on the market. Con-

sidering the requirements of a 3D modeling and scene

interaction application, we need a device that offers as

much spatial and temporal accuracy as possible.

417

Siegl C., Süßmuth J., Bauer F. and Stamminger M..

Evaluating the Usability of Recent Consumer-grade 3D Input Devices.

DOI: 10.5220/0004653404170423

In Proceedings of the 9th International Conference on Computer Graphics Theory and Applications (GRAPP-2014), pages 417-423

ISBN: 978-989-758-002-4

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

The ﬁrst device that comes to mind when thinking

about tracking human poses is the Microsoft Kinect

sensor (Microsoft Corporation, 2010) (see Figure 1).

Being one of the ﬁrst affordable consumer devices in

this area it is widespread. The Kinect is suitable for

tracking the entire human body. However, it does not

offer sufﬁcient resolution for tracking individual ﬁn-

gers. Additionally, the maximum frame rate at the

highest resolution is only 30 frames per second.

A similar device is the SoftKinetic (Intel Corpo-

ration, 2013) (see Figure 1). The SoftKinetic works

in a range from 0.1m to 1.1m. It is geared towards

tracking faces and hands, with frame rates around 30

frames per second.

The most recent device that is completely geared

towards tracking hands is the Leap Motion con-

troller (Leap Motion, Inc., 2012) (see Figure 1).

While the Leap launched on July 22, we got a pre-

production unit to work with. The Leap sensor is

able to determine the location and orientation of the

users hand with sub-millimeter precision. Compared

to the other devices it provides the highest accuracy in

the tracking of hands and ﬁngers, and operates at up

to 295 frames per second. Those speciﬁcations look

promising enough to envision the use of this device in

intuitive and interactive 3D modeling.

1.2 Previous Work

3D user interfaces have been an active topic in the

computer vision and graphics communities and many

interesting papers have been published over the last

decades. For a broad overview over 3D spatial in-

teraction, we refer our readers to the Siggraph 2011

course notes on “3D spatial interaction: application

for art, design, and science” (LaViola and Keefe,

2011) and references therein.

While over the last years, a lot of research has

been targeted at large scale 3D body gesture recog-

nition using the Kinect Sensor (see (Ren et al., 2011;

Gallo et al., 2011) and references therein), only little

effort has been put into assessing the abilities of 3D

interfaces for 3D object modeling and manipulation.

Using custom hardware, one very advanced ap-

proach towards modeling using 3D interaction was

conducted by Araujo et al. (Araujo et al., 2013) by

showing their tool “Mockup Builder”. They use a

multitouch screen with 3D projection, a Kinect and

mechanical tracking of positions in space. This com-

plicated setup remedies a lot of problems we encoun-

tered but the sheer amount of hardware does not seem

feasible to us.

Hilliges et al. (Hilliges et al., 2009) have imple-

mented a system (hard- and software) that combines

a multitouch device with depth information. In their

system they can pinch an object and move it in 3D.

They encountered similar problems with depth per-

ception we will describe later on (see Section 4.2).

In their application the scene is presented to the user

from a birds eye view, therefore shadows are sufﬁ-

cient to correctly gauge relative depth.

Ren and O’Neill (Ren and O’Neill, 2013) address

the problem of 3D selection using freehand gestures.

They state that a single action for this task is not feasi-

ble because of accuracy issues. In their work they pro-

pose that a series of low level movements improves

the precision of the selection. To get a better un-

derstanding for this we recommend the survey paper

of Argelaguet and Andujar (Argelaguet and Andujar,

2013). This is a special case of handling clicking ges-

tures. We will describe this in a more general manner

later on.

Another approach that was taken in the past to-

wards an accurate retrieval of hand gestures are HMI

(human machine interface) gloves. See for example

the work of Saggio et al. (Saggio et al., 2010). These

gloves are equipped with sensors at every joint recov-

ering every movement. They allows for a very accu-

rate retrieval of relative ﬁnger positions. However the

absolute position of the hand in 3D still can not be de-

termined. Also the usability suffers and the price of

such systems is quite high.

In this context, one last bit of related work we

want to mention is the work with haptic feedback de-

vices like for example the PHANTOM (Massie and

Salisbury, 1994). Using such a device the user holds

a pen that is connected to a “robot arm”. This enables

a very accurate tracking and allows the user to per-

form a clicking gesture through a button on the pen. It

also provides haptic or tactile feedback. Given such a

device many of the problems we will encounter later

on are remedied. However we want to concentrate

on working with affordable, off the shelf, consumer-

grade hardware.

2 DEVICE LIMITATIONS

Given that we want to work with affordable

consumer-grade devices, challenges due to device

limitations have to be addressed. As our primary

device we use the pre-production Leap Motion con-

troller. With this setup we can identify two major

problems, a limited ﬁeld of view and occlusion.

The ﬁeld of view (in case of the Leap, a cone with

an opening angle of approximately 90

◦

) is especially

problematic as the user can neither see it nor gets

feedback upon leaving it. Given the restricted inter-

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418

action space it is not feasible to use a second hand

inside the view frustum (as was often suggested by

users) as we simply would run out of space. In partic-

ular moving one hand will often occlude the other.

Devices that need a line of sight to the tracked

hand, do not work in case of occlusion. That means,

while working with two hands, one cannot be above

the other. This also applies to individual ﬁngers as can

be seen in Figure 2. On the left side, the tracking will

work, on the right side, with two ﬁngers only slightly

moved up and down, the tracking will fail for these

two ﬁngers. Also the whole ﬁnger has to be seen from

below. As soon as the ﬁngertips point too far down-

wards, tracking will fail. Touching of ﬁngertips in a

pinch gesture will also lead to failure of tracking for

both ﬁngers.

Figure 2: Using the Leap device robust hand tracking only

works while every ﬁnger can be seen separately from the

device (left side). The conﬁguration on the right will lead

to jittering of the middle and ring ﬁnger positions or a com-

plete loss of the tracking.

3 SCENE INTERACTION

When ﬁrst experimenting with our Leap device, we

started to evaluate very simplistic three-dimensional

gestures. We implemented a prototype application for

camera navigation that provides the opportunity to in-

teract with a simple 3D scene (see Figure 3).

For the camera control we wanted to achieve a

ﬂight-like interaction. Imagine your hand as an air-

plane. By moving this plane you control the cam-

era. This seemingly simple task already revealed one

fundamental problem. It is not obvious to determine

whether the user wants to interact with the camera or

merely wants to reposition the hand. To circumvent

this we have to deﬁne an activation or clicking ges-

ture. In our application this is implemented by a grab

motion.

Using the Leap API, we can implement this ges-

ture by monitoring the radius and center of a sphere

that is ﬁtted into the palm of the hand. The range of

hand positions for which this sphere ﬁtting works is

quite narrow (see Figure 4). However, after a bit of

training this gesture can be performed quite well.

The result of this free-ﬂight interaction is an easy

to control camera that can perform very nice and

Figure 3: A screenshot from our application for scene inter-

action.

Figure 4: The grabbing feature only works in a narrow band.

In the upper two images the gesture will work. For the lower

two images the gesture will fail. While the left failure case

could probably be resolved in software, the right failure case

is more severe. Since the sensor only records the scene from

a single direction, the palm of the hand is occluded by ﬁn-

gertips.

smooth pans that would not be possible by conven-

tional input using mouse and keyboard (see video).

We found that this is also very intuitive for ﬁrst-time

users.

The second functionality implemented in this ap-

plication is scene manipulation. For picking objects

we tried to utilize the well known pinch motion. This

gesture however is not robust using the Leap device.

As the ﬁngers come close to each other the tracking

fails. Therefore, we use the “cross ﬁngers” gesture

shown in Figure 5. Having the ﬁngers crossed (left

image) corresponds to not having the mouse button

pressed, while having separated the ﬁngers (right im-

Figure 5: As a pinch gesture is not possible we substituted

it with this crossing of ﬁngers. In the left conﬁguration the

Leap will only recognize one ﬁnger. By separating the two

ﬁngers it is possible to select a model inside the scene.

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419

age) corresponds to holding the mouse button down.

While the user has his ﬁngers crossed, we highlight

the object that is closest to the position described by

the two ﬁngers. The user can then pick the highlighted

object by separating the ﬁngers and move it around.

To drop the object, the user has to cross his ﬁngers

again. This is similar to drag and drop in mouse inter-

action.

The problem with the activation and clicking ges-

tures is their accuracy. It is generally not possible

to perform a hand movement for triggering a gesture

without moving the hand. This however leads to an

unwanted alteration of the camera or the scene. No

matter how accurate the tracking device, accuracy of

the interaction is impaired. In our opinion this is a

fundamental problem of 3D input devices.

Given this rather simple application the Leap was

able to perform quite well for navigating the scene.

While some technical limitations became apparent it

was possible to create an application that was sufﬁ-

ciently easy to use. However when trying to navigate

to an exact position (aligning a cross in the scene with

a cross on the monitor) we had problems to perform

the ﬁne grained alterations necessary to achieve a per-

fect alignment. This underlines the general problem

of 3D interaction schemes having insufﬁcient preci-

sion when using freehand gestures. When discussing

our modeling application in the following chapter this

will become more apparent.

4 MODELING

We ﬁrst started with the idea of implementing a mod-

eling application using true three dimensional input.

Our goal was to model simple chess pieces, as these

are widely known and easy to create. After observing

the deﬁciencies regarding precise camera positioning,

we decided to focus our analysis on accuracy.

In the resulting application, the user can build

rotationally symmetric 3D models. Initially only a

cylinder exists that is deﬁned by two rings at the top

Figure 6: Modeling of rotationally symmetric models. Con-

trol mesh on the left, Catmull-Clark subdivided mesh on the

right.

and bottom. Using hand gestures only, additional

rings can be added, existing rings can be moved up

and down and their radius can be modiﬁed. To get

a visually more pleasing result we use Catmull-Clark

Subdivision on the resulting base mesh (see Figure 6).

4.1 2D Interface

The 3D cursor can be positioned by moving the hand

up and down. When the hand is moved away from

the screen and penetrates a predeﬁned plane, a new

editable ring is added. To select an existing ring, the

hand has to be moved closer to the screen and again

penetrate a predeﬁned plane. While a ring is selected

it can be resized by moving the hand left and right

and repositioned by moving the hand up and down. In

this ﬁrst stage we explicitely do not use real 3D inter-

action and therefore used the “gloriﬁed touchscreen”

interface many other applications implement instead

of true 3D interaction. However one problem that be-

came apparent is the fact that users can not see or feel

those planes. Therefore those users do not know the

relative position of their hand and the planes, mak-

ing it hard to select and deselect rings in a predictable

way.

We evaluated this problem by giving the task to

model a simple chess piece to a small group of users.

The chess piece of our choice was the pawn shown in

Figure 7(a), as this piece needs no further 3D editing

and can be modeled with the application we have de-

scribed so far. Each user had to model the same pawn

twice, once using solely hand gestures, and once us-

ing a modiﬁed version of our modeler, where the hand

gestures for creating a new, selecting an existing and

deselecting the current ring were replaced by single

key presses.

As we have already mentioned, participants had

trouble applying the selection and deselection ges-

tures without side effects. Especially deselecting a

ring often led to unwanted alterations of the model.

As a result the users became hesitant and lost con-

ﬁdence in achieving a good result. As soon as

their model looked close enough to the template they

stopped editing in anticipation of destroying the result

with further editing steps.

Given the keyboard to substitute the clicking ges-

tures, the average time per model did not change no-

ticeably. However all users felt up to the task to

add additional detail to the model (without encour-

agement from us). They also corrupted their model

less often and no longer needed to restart the model-

ing process (this is the reason for the longer modeling

time of User 4). The results are closer to the template

model than those obtained using only hand gestures

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420

(a) Target (b) 224s vs. 215s (c) 72s vs. 52/102s (d) 176s vs. 163s (e) 470s vs. 153s

Figure 7: Some results of modeling a pawn. Users (with minor background in 3D modeling and without prior training) were

given the task to model a pawn. The blue models were created using gestures for picking, the red models using keystrokes.

The time-lapse session for user 2 can be found in the accompanying video. The second timings for this user show the time it

took for modeling the base (comparable to the blue pawn) version and the reﬁned version with additional detail.

as can be seen from Figure 7.

To get a better understanding why the users had

problems triggering the interaction we investigated

the problem: Selection and deselection is determined

by penetrating a predeﬁned, invisible plane. In order

to select a ring, the user is moving his hand towards

the screen. When intersecting our given plane, the

software is entering the edit mode, thus giving feed-

back of a successful interaction. While editing the

rings, the hand has to be moved while it stays be-

hind the interaction plane. Intuitively the user will

move his hand along an imaginary plane (blue plane

in Figure 9), however this plane usually does not cor-

respond with the one deﬁned by the Leap coordinate

frame (yellow plane in Figure 9). This results in the

user unexpectedly changing the depth of the penetra-

tion having two observable side effects. If the user ex-

pects the tip of his ﬁnger to be well beyond the plane

of interaction he might accidentally quit the editing

mode. Even worse if he expects his ﬁnger to be close

to the plane of interaction and wants to exit the edit-

ing mode, he will unexpectedly alter the model while

having to move the hand further than expected. The

problem could be avoided if the plane that the user is

supposed to interact with would be sense-able.

4.2 3D Interface

Since most chess ﬁgures are not rotationally symmet-

ric, we extended our application for true three dimen-

sional vertex manipulation. Users can pick vertices

using the “cross ﬁngers” gesture shown in Figure 5.

While a vertex is selected, the user can set its true 3D

position by moving the hand. We use this extension to

model the crown of the queen chess piece. The users

have to extrude the prongs of the crown by picking

a vertex of the base mesh and moving it perpendic-

ular to the surrounding surface. We found that there

are some problems with this seemingly intuitive mod-

eling approach. The ﬁrst one is the inaccuracy with

picking we discussed in Section 3. The second one

is the poor depth perception that makes an accurate

movement along a three dimensional line very hard.

5 RELATIVE LOCALIZATION IN

Having been surprised by the fundamental problems

we ran into we wanted to investigate these issues fur-

ther. For this purpose we implemented a simple voxel

carving application with two different stencils. A

sphere representing the tip of a pen and a box as a

very crude representation of a hand.

Our ﬁrst aim was to evaluate the lack of depth per-

ception we described in Section 4.2. We asked some

users to carve a music note with a penetration of con-

stant depth into one side of the voxel cube using the

spherical carving tool. In order to also evaluate the

problem with the tilted imaginary plane we described

in Section 4.1, we rotated the voxel cube such that

its vertical sides are not parallel to the screen aligned

coordinate planes. This means correct movement in

all three dimensions is necessary to keep a constant

penetration depth.

With just the sphere as visual cue for the position

of the tool it was virtually impossible for the users

to correctly gauge the relative depth of the sphere to

the voxel volume (see 8). This underlines the nega-

tive impact of a lacking depth perception. To gauge

how much help is needed to get a convenient user in-

terface we implemented different levels of assistance

that were given to the users.

The ﬁrst and most obvious additional clue is vi-

sualizing the depth level of the sphere by rendering a

plane through its center parallel to the screen. This

way the user can see at which depth the sphere will

hit the voxel cube before it touches (please refer to

the accompanying video for more details). While this

helps, it is still hard to perceive an accurate depth or

know exactly when the sphere starts intersecting the

volume and even more to gauge the depth of the actual

penetration.

In the real world we have tactile feedback and re-

sistance from a surface to help us carving at a con-

stant depth. While tactile feedback can be provided

by haptic devices like the Phantom (Massie and Sal-

isbury, 1994), we wanted to concentrate one less ob-

EvaluatingtheUsabilityofRecentConsumer-grade3DInputDevices

421

Figure 8: Results of our carving experiment. One user

per row. From left to right: only visual feedback, audio

feedback and snapping. Results are color coded by carving

depth.

trusive, low-cost consumer devices mentioned in the

introduction. As a work-around, we use acoustic in-

stead of tactile feedback. We output sound similar to

park distance control in modern cars when the tool is

outside the voxel volume. The interval between beep-

ing sounds becomes shorter as the tool approaches the

volume until a continuous sound is played upon con-

tact. A second form of acoustic feedback is provided

for the penetration depth of the carving tool inside the

volume. This is encoded by changing the pitch of the

continuous sound.

When comparing the results using visual and

acoustic feedback, it can easily be seen that all users

had severe problems carving at a constant depth with

visual hints only (see Figure 8, red color marks areas

where the carving depth does not conform to the tar-

get depth). When provided with acoustic feedback,

the users had less problems, however, the strokes are

still shaky. Given the many degrees of freedom, it is

difﬁcult to simultaneously concentrate on penetration

depth and stroke direction.

Since the results were still not satisfying, we

implemented a snapping aid constraining the users

movement to one side of the voxel cube. This concept

is very similar to the modeling aids provided in cur-

rent modeling tools that use mouse and keyboard as

interaction devices. Our snapping aid restricts move-

ment of the carving tool onto a plane aligned with the

axis of the voxel volume. Therefore, we only consider

the user interaction in x- and y- direction and discard

the hand movement in z-direction. Please note that

this is also a reduction of the dimensionality of the

problem as only two out of three available degrees of

freedom are used as input and should (in theory) cor-

Figure 9: Restricting the user interaction to a 2D plane

causes new problems: Since the plane in which the user

interacts will hardly ever coincide with the coordinate sys-

tem of the input device, projecting the user input onto this

plane will distort it.

respond to well known mouse interaction semantics.

When the carving tool was snapped to one face

of the cube, the users became much more ﬂuent in

their interaction, which led to notable better results.

The video accompanying this paper shows how the

interaction gets faster and less shaky. However the

resulting notes still show some shakiness with the re-

duced degree of freedom, leading to the suggestion

that those shaky paths are not caused by the additional

degrees of freedom the Leap introduces.

When recording user gestures with a 3D input de-

vice, we expect the user to move on a predeﬁned plane

(yellow plane in Figure 9). The user intuitively will

penetrate the plane with a certain depth and move

along an imaginary plane (see Section 4.1). As the

predeﬁned plane can neither be seen nor felt, it is

very likely that the plane imagined by the user (blue

plane in Figure 9) does not correspond to the expected

one. Given this conﬁguration the user input will be

distorted by a projection of the shape drawn on the

imagined plane on to the expected one. For example,

as depicted in Figure 9, the slope of a line drawn by

the user will not be as expected (compare angles α

and β). This effect is the reason for shaky results (see

third column in Figure 8, the ﬁrst bumps on the left

side of the horizontal line), as the user is adjusting the

slope while drawing. Vertical lines are not affected

by this projection as the cube is only rotated along the

vertical axis. This means the slope of the expected

vertical line is identical to the carved one.

The problem could be avoided if the plane that

the user interacts with would be known to the system,

as the projection causing the observed discrepancy in

slopes is neutralized. Unfortunately, this plane can-

not be predicted for two reasons: First, the user most

likely does not even interact on a plane but rather on

a curved surface. Second and more importantly, to

predict the plane from the user interaction, we would

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422

have to gather several frames to analyze the motion.

However, this contradicts the real-time feedback that

is required for interactive 3D modeling.

In order to increase depth perception in general,

we used a 3D-TV. We found that the additional depth

cue provided a level of assistance comparable to the

acoustic feedback. However, a problem of this setup

is that the coordinate system where the virtual scene

is seen and the coordinate system where the hands

move in still do not coincide. This causes another in-

direction which counteracts an intuitive interface and

is confusing for novice users. The introduction of fu-

ture virtual reality glasses with large view frustums

like for example the Occulus Rift could potentially

help solving this problem. This headset would allow

us to merge the coordinate systems of the hand and

the scene (assuming a stable tracking).

6 CONCLUSIONS

In this paper, we have examined the viability of real

3D interaction using recent consumer hardware, espe-

cially the Leap Motion controller. In our tests it be-

came apparent that there are three fundamental prob-

lems. The lack of a clicking gesture, bad depth per-

ception with standard displays and the discrepancy

between the plane of interaction assumed by the soft-

ware and the user.

As it turns out the precision of true 3D interaction

is not limited by the resolution of the input device.

In fact the Leap provides enough temporal and spatial

resolution to support all our modeling needs. The im-

precision in the modeling procedure is inﬂicted by the

misalignment of the imagined and the actual coordi-

nate system, as we showed in Section 5.

When interacting with a mouse the hand is sta-

belized by the table top. Using 3D interaction, it has

to ﬂoat above the table forcing the muscles to retain

the position. This is another source of imprecision as

the hand will move unintentionally.

In conclusion we think that the viability of true

three dimensional interaction is not limited by hard-

ware at the current point of time. The limiting factor

rather is the software processing and interpreting the

input.

ACKNOWLEDGEMENTS

We would like to thank the BlendSwap community

for the models we used in our scene interaction appli-

cation. The Space Marines were created by hjmedias-

tudios, the House by julsengt.

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