mimicking hand-drawn pencil lines · z. meraj & b. wyvill & t. isenberg & a. gooch...

Computational Aesthetics in Graphics Visualization and Imaging (2008)P Brown D W Cunningham V Interrante and J McCormack (Editors)

Mimicking Hand-Drawn Pencil Lines

Zainab Meraj1 Brian Wyvill1 Tobias Isenberg2 Amy A Gooch1 Richard Guy3

1University of Victoria BC Canada 2University of Groningen Netherlands 3University of Calgary AB Canada

Abstract

In applications such as architecture early design sketches often mislead the target audience [SSRL96] Approxi-mate human-drawn sketches are typically accepted as a better way of demonstrating fundamental design conceptsTo this end we have designed an algorithm that creates lines that perceptually resemble human-drawn lines Ouralgorithm works directly with input point data and physically based mathematical model of human arm movementFurther the algorithm does not rely on a database of human drawn lines nor does it require any input otherthan the end points of the lines to generate a line of arbitrary length The algorithm will generate any number ofaesthetically pleasing and natural looking lines where each one is unique The algorithm was designed by con-ducting various user studies on human line sketches and analyzing the lines to produce basic heuristics We foundthat an observational analysis of human lines made a bigger impact on the algorithm than a statistical analysisA further study has shown that the algorithm produces lines that are perceptually indistinguishable from straighthand-drawn pencil lines

Categories and Subject Descriptors (according to ACM CCS) I3m [Computer Graphics] MiscellaneousmdashNon-photorealistic rendering natural media simulation pencil rendering dynamic optimization yielding voluntary armmovement trajectory image processing

1 Introduction

Non-photorealistic Rendering (NPR) can convey informa-tion more effectively by omitting extraneous detail (abstrac-tion) by focusing the viewerrsquos attention on relevant features(emphasis) and by clarifying simplifying and disambiguat-ing shape In fact a distinguishing feature of NPR is the con-cept of controlling and displaying detail in an image to en-hance communication The control of image detail is oftencombined with stylization to evoke the perception of com-plexity in an image without its explicit representation NPRimagery allows the

bull communication of uncertaintymdashprecisely rendered com-puter graphics imply an exactness and perfection that mayoverstate the fidelity of a simulation or representation and

bull communication of abstract ideasmdashsimple line drawingssuch as diagrams used in textbooks can communicate ab-stract ideas in ways that a photograph cannot

While there is no precise metric to differentiate betweenhand-drawn and computer-generated line drawings (some at-tempts have been made for specific techniques [MIAlowast08])

humans can typically distinguish with ease the difference bya shear glance For pencil lines this may be due to changesin grey levels a variation not proportional to the path or achange in the path orientation Such variations make it diffi-cult to produce aesthetically pleasing natural looking resultsthat mimic human-drawn lines

Our method is based upon observation and statistical anal-ysis of hand-drawn lines in conjunction with a model ofhuman arm movement to create unique linesmdashgiven onlya start and an end point without the use of a large sampleline database In addition our method does not require thesetting of user-determined parameters (patterns of deforma-tion pressure density etc) The only parameter users are re-quired to select through the system interface is one of eightcommonly used pencil types Our method then formulatesand reproduces a curvature and texture that conforms andmimics real human-drawn pencil lines

The goal of this research is to capture the essence of a sin-gle stroke drawn by humans as straight pencil lines of arbi-trary length and encode it into an algorithm In turn an ap-

copy The Eurographics Association 2008

Z Meraj amp B Wyvill amp T Isenberg amp A Gooch amp R Guy Mimicking Hand-Drawn Pencil Lines

plication may use this algorithm to produce lines that resem-ble human-drawn lines (e g [Bre65]) Ideally such an algo-rithm would reproduce the details carried by a human-drawnpencil line without the need of storage libraries or user inputto specify line attributes (as is the case e g with [SB00])Since the lines do not have set widths colors or particulartextures our proposed method will approximately reproducethe pencil details within the stroke it follows In additionsuch a line should not have repeated sections so that eachline is unique

We divide our algorithm for generating a human-like pen-cil lines into two parts (a) synthesizing the path that cor-responds to human arm movement and (b) synthesizing thepencil texture applied along the path inspired by the texturesproduced by real pencils Based on this general approachour algorithm

bull produces high quality simulations of hand-drawn lines

bull easily incorporates into existing applications

bull produces lines of arbitrary length

bull does not require a library of sample lines (only a grey leveldynamic range array and a co-occurrence matrix) and

bull creates unique lines for every set of input point pairs

Our contribution is a high quality pencil media line reproduc-tion agent for creating aesthetically pleasing lines that mimichuman-drawn lines For this purpose we use methods of im-age synthesis and a model of human arm movement for itsreplication Our method avoids computationally expensivetechniques and large storage space while continuously pro-ducing new unique lines

2 Previous Work

Our work draws from research on interactive non-photorealistic rendering (NPR) methods that approximateartistic hand-drawn images or paintings In particular wedraw from NPR approaches for generating human line draw-ings and the simulation of graphite pencil texture texturesynthesis and literature on the trajectory of human armmovement while drawing

21 Human Line Drawings

Characteristics of lines in sketched and hand-drawn imageshave been studied closely in the field of NPR Many algo-rithms captured style characteristics and applied multipleparameters (such as length width pressure etc) Previousmethods [HL94 SSSS98 SS02] used style parameters anddistorted a textured predefined piecewise polynomial curveor polygon path to create a stylized line Other approachesreproduced and synthesized similar styles from exampleslines [JEGPO02 KMMlowast02 FTP03 FS94 SD04 Bru06]

Our method differs from example-based methods in that we

do not require example lines to generate unique paths andtextures For each pencil type there exists two pieces ofinformation in the algorithm a dynamic range and a co-occurrence matrix We simply require vector endpoints toproduce lines Our aim is not to generate varying style froma single given style as seen in example-based methods butto develop an algorithm that generates lines that vary in pathorientation and texture synthesis mimicking observed realhuman movement and graphite pencil deposits noticed fromstraight line drawings on paper

Our work is inspired by methods that simulated pencils asa medium specifically the work by Sousa and Buchanan[SB99 SB00] who contributed a low-level simulation ofgraphite pencils on a paper medium They designed a sys-tem to simulate graphite pencil on paper using microscopicobservations [SB99] Their work focused on generating fillstrokes using it for hatching purposes to reproduce artisticdrawings Our method differs because our lines are not re-stricted to short strokes and can vary greatly in length withno repeating segments We also base our work on interac-tive pen-and-ink illustration by Salisbury et al [SABS94]who described a level-of-detail system that interactively pro-duces multiples of strokes to avoid tediously placing themmanually Our algorithm for drawing lines could easily beincorporated into the above approaches adding the benefitof a model of human arm movement and a simple perceptualsimulation model for graphite pencils without the require-ment of a library of lines to copy paste and reshape

22 Texture Synthesis

We were also influenced by a texture synthesis method basedupon sequential synthesis [GM85] that preserves the sec-ond order statistics of the natural texture into the newlysynthesized texture Gagalowicz and Ma [GM85] also pro-vided experimental evidence that the visual system is onlysensitive to second-order spatial averages of a given tex-ture field More recent texture synthesis research renamessecond order statistics (spatial averages) using the term co-occurrence (CC) models or grey level co-occurrence (GLC)models [CRT01] We use the GLC model which is definedas the proportion of second order probability statistics ofgrey levels pairs that differ by a delta in the texture

Applying co-occurrence matrices to synthesize texture havebeen shown to be extremely efficient in creating artificialtextures that are hard to discriminate from in real textures[JB87] the amount of data necessary to achieve the synthesisis very small and the texture can be generated easily to fit allkinds of surface shapes and sizes Using this method allowsus to control the second-order spatial averages of a given tex-ture since the synthesis is achieved directly from them with-out the computation of higher order statistics [GM85] Alsotexture similarity techniques using GLC probability distribu-tion have shown to have high correlation with human percep-



tion of textures (the images appear to be visually similar tothe original natural images)

Copeland et al [CRT01] used a texture similarity metricbased on the texture field of a model texture The multi-resolution version of their algorithm ldquoSpin Fliprdquo showedsatisfactory performance and resulted in pleasing outputs

Zalesny and Gool [ZG01] introduced a similar texture syn-thesis method based on simulation of image intensity statis-tics They collect the first order statistics (an intensity his-togram) then extract the co-occurrence matrix (CCM) usingcliques i epair of points (a head and a tail) Zalesny andGool apply an additional criteria for classifying groups ofcliques using the CCM to store the distribution of intensitydifferences between the heads and tails pixels for a given ori-entationWe found this latter criteria was not necessary forour approach Our CCM is calculated by summing all thejoint probabilities of intensities for a given pixel neighbor-hood into their relative position in the CCM

23 Dynamic Optimization of Human Arm Movement

In order to produce a good simulation of a human drawnlines we have also examined studies of the coordinationof voluntary human arm movements Human motor pro-duction has been analyzed modeled and documented forwell over a century [Woo99Ber67Ada71Sch75] Over thepast few decades theories of the functions of the CentralNervous System (CNS) with respect to human arm move-ment lead to the hypothesis of various mathematical mod-els [FH85 UKS89 BMIG91 MAJ91 KG92 CVGB97]

According to these CNS theories arm movements are pro-duced in either one of two ways

bull Natural movements maintain a constant ratio of angularvelocities of joints to bring reduction in control complex-ity and constrain the degrees of freedom

bull Hand trajectories are in extra-corporal space joint rota-tions and additional non-physical parameters are tailoredto produce the desired or intended hand movements

Plamondonrsquos model [Pla95] describes a synergy of agonistand antagonist neuromuscular systems involved in the pro-duction of arm movements He developed his theory by mod-eling the impulse responses to neuromuscular activities Hissystem produces a close proximity bell-shaped velocity pro-file to represent an entire point-to-point movement

The minimum jerk model introduced by Flash and Hogan[FH85]formulated an objective function to solve a dynamicoptimization problem for measuring the performance of any-possible movement as the square of the jerk (defined as thechange rate in acceleration) of the hand position integratedover an entire movement from start to end positions Flashand Hogan [FH85] showed that the unique trajectory of pla-nar multi-joint arm movements that yields the best perfor-

mance was in agreement with experimental data Their anal-ysis was based solely on the kinematics of movement andindependent from the dynamics of the musculoskeletal sys-tem as in the work done by Plamondon [Pla95] We adopt theFlash and Hogan model because the model represents humanarm trajectory in a planar field similar to the movement of ahuman hand guided pencil across a piece of paper

3 Algorithm

There are two parts to this work The first constructs an algo-rithm to generate a realistic path The second synthesizes asuitable texture to mimic a human line using a specific pencilOur technique analyzes both the path and the stroke texture

31 The Path

One of the hardest parts of replicating a human line draw-ing is creating the natural variation in the path of the lineWe construct a path that conforms to a human arm trajec-tory using the method described by Flash and Hogan [FH85]This method provides ldquothe smoothest motion to bring thehand from an initial position to the final position in a giventimerdquo [FH85] Our goal is to simulate a hand-drawn line onlygiven two points The Flash and Hogan model produces tra-jectories that (1) are invariant under translation and rotationand (2) whose shape does not change with amplitude or dura-tion of the movement All of these characteristics are basedon observations of low frequency movements of human sub-jects No high frequency movements were observed or im-plemented in the Flash and Hogan model Our work adoptsthe same assumptions

The Flash and Hogan mathematical model leads to a fast andinteractive line path algorithm by providing a realistic over-all velocity and acceleration profile and trajectory

Our lines are defined by Equation 1 a fifth order polynomial

x(t) = x0 +(x0minus x f )(15τ4minus6τ

5minus10τ3)

y(t) = y0 +(y0minus y f )(15τ4minus6τ

5minus10τ3) (1)

where

τ =tt f

t isin [02]

in which x0y0 are the initial hand position coordinates attime increment t and x f y f are the final hand positions at t =t f The value of t varies within [02] from the start position t0to the end position t f inal We found empirically that t f inal = 2provides satisfactory results regardless of line length

Line orientation is not present in the mathematical modelbut we conducted user studies that indicate the disparityof the line orientation was not noticed by participants anddid not effect the classification of real versus computer-generated drawings



(a) Short line

(b) Medium Line

(c) Long line

Figure 1 Generated lines and control point positions forvarying lengths For (a) ∆t = 05 for (b) ∆t = 03 and for(c) ∆t = 02 The red dots represent the control point

The points resulting from the polynomial of Equation 1 pro-vide control points for a Catmull-Rom interpolating spline[SAGlowast05] The number of points (defined by the time step∆t) depend on the length of the line Experimental evidence[FH85] shows that short hand drawn lines are perceptuallycloser to straight lines and longer lines have more variationWe conducted experiments to find reasonable values for ∆tand provide the results in Table 1 Figure 1 shows the posi-tions of the control points for three lines of varying lengthusing each of the prescribed values for ∆t

We conducted a pilot study in which the participants wereseated upright in front of a horizontal table with no con-straints Each participant drew a number of pencil lines be-tween different orientation pairs of points

To introduce a variation across the line we include a devia-tion parameter based on observations of how humans drawlines The data we collected showed considerably more vari-ation from the centre line that wasnrsquot accounted for with thevariation of trajectories assumed by Equation 1

To represent this variation we introduce a deviational param-eter we call squiggle as an extension to the Flash and Hoganmodel The additional term is necessary to provide suffi-cient variation in the line paths based on our observationsof human drawn drawings Experiments were conducted tovalidate this choice Section 5 provides details on these ex-periments We define squiggle as a variable D for control-ling the deviation normal to the Catmull-Rom spline at eachof the lines control points also similar to methods used byStrothotte et al [SS02] The magnitude of the squiggle pa-rameter controls the magnitude of random displacements ap-plied to the Catmull-Rom spline controls points and there-fore strongly influences the appearance of the path

The deviational value is applied approximately normal to theCatmull-Rom spline at each of its control points The vari-able D varies randomly in order to provide variation alongthe path we empirically found a range [-5+5] worked forour lines regardless of length and produces independent dis-placement values for each control point

approx Line Length in pixels Time step ∆t[0200] 05

(200400] 03gt 400 02

Table 1 Empirical Values for the time step ∆t

32 The Texture

For each pencil type we conduct a one time analysis of ex-ample lines in order to create a statistical profile encoded ina histogram and CCM Our algorithm creates the line textureafter the natural-appearing path is created by initializing thestroke with random values applying a bell-shaped fall-off ofintensities from beginning to end of the stroke applying theCCM for the pencil type and then applying a Gaussian filter

First we obtain texture for simulating hand drawn lines byscanning and analyzing the statistical properties of examplelines drawn by human participants using a wide range of pen-cils of the following types 2H H HB F B 3B 6B 8B (ex-amples are shown in Table 2) on plain 100 recycled whitepaper The following steps are taken to correctly capture thetextural properties of the model texture

bull First we determine the range of grey levels for pixelsalong the approximate centre of the pencil line (non-repeating Catmull-Rom control points) and record the his-togram for the range [minmid maxmid ]

bull Next we determine the histogram of the grey levels forthe line image assuming that white pixels surround eachline segment since very few white pixels appear insidethe line image we do not consider white in the his-togram The histogram records intensities in the range[minrangemaxrange]

bull Finally we compute the co-occurrence matrix for the lineimage

To determine the co-occurrence matrix (CCM) each imagescan is rotated so that the line is approximately vertical Theco-occurrence matrix is updated by examining each pixel(pq) with value i isin [0255] and itrsquos immediate neighborin the positive y-direction (pq + 1) with value j isin [0255]The values (i j) are indices to increment the appropriate cellof the co-occurrence matrix C defined over an n x m imageI parameterized by an offset (∆x ∆y) as in Equation 2

C(i j) =n

sump=1

m

sump=1

1 i f I(pq) = i and

I(p+∆xq+∆y) = j0 otherwise

(2)

We use the dynamic range and the co-occurrence of a targetpencil texture to synthesize new the texture We formulatea grey value distribution technique according to statisticalobservations that indicate for most pencil lines the distribu-tion of grey pixel values starts at a dark value in the middle



of the line and falls off according to the bell-shaped curve inthe direction normal to the line see Figure 2

Cross Section of a 6B Pencil Line

0

50

100

150

200

250

300

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37

Width of The Line ( pixels )

Gre

y L

evel

Inte

nsi

ties

Figure 2 The approximately bell-shaped curve showing in-tensity values of cross sections of a lines drawn with a 6Bpencil

We first seed the line with initial random values (using thepre-selected pencil type) and then apply the co-occurrencematrix According to the bell-shaped fall off we observedwe distribute the grey levels of the centre pixels of thegenerated spline replicating the histogram in the range[minmid maxmid ] We determine an approximate directionnormal to the line and the pixels along this direction to ei-ther side of the centre pixel are set to values randomly cho-sen in the range [minrangemaxmid ] and pixels further awayare given a lighter intensity in the range [maxmid maxrange]as illustrated in the texture sample of Figure 3

Figure 3 Initial synthesized line texture and partial close-up view of the line texture Placing the grey dynamic rangevalues across the width and length of the path uniformly

Next we apply the CCM by comparing a 3 times 3 neighborhoodof pixels If the combination of pixel intensities does not ex-ist in the CCM each pixelrsquos grey value is replaced with an ex-isting neighboring value from the co-occurrence matrix Werepeat the co-occurrence process over the entire line multipletimes until the amount of pixel changes reach a minimum in-dicating how well the synthesized co-occurrence matrix rep-resents the model CCM (see Figure 4)

Once complete a single-pass Gaussian filter is applied tothe generated lines to reduce tone differences and filter outaliasing effects (Figure 5)

Figure 4 Line and partial close-up view of the line tex-ture after CCM step The values of the pixels are analyzedand pairs of pixels are compared to their indices in the co-occurrence matrix and replaced with an appropriate value iftheir pair does not exist

Figure 5 Final pencil line and close-up view of the line aftera 3 times 3 single-pass Gaussian filter is applied

4 Results

Our Human Line Algorithm (HLA) is implemented in C++and runs on a 200 GHz Intel dual core processor worksta-tion without any additional hardware optimization We caninteractively draw lines while the system synthesizes the newpath and texture All line drawing figures in this paper aredrawn with our system when ldquogenerated by HLArdquo is indi-cated Table 2 shows a comparison of hand-drawn lines withlines that were synthesized by our system

The following is a pseudo-code description of the HLA algo-rithm to enable easy application of the method

1 initialize the CCM and histogram to the user-selected pen-cil type

2 read in endpoints and3 if the number of endpoints equals 2 thenbull find the control points of the line between the two

specified end pointsbull distribute random grey values across amp along the linebull use the CCM to replace random grey values along the

line in 3 times 3 neighbourhood with existing co-locatedneighbour values in the CCM and

bull Gaussian-blur the line

5 Verification

In order to evaluate our technique we designed a study usingeighteen images nine of which were scanned human-madedrawings and the other nine were exact replications of thescanned images made using our line algorithm The aim ofthis study was to evaluate whether our generated lines wouldpass for real human-drawn lines Eleven participants tookpart in our study all graduate and undergraduate students inthe department of Computer Science at the University of Vic-toria Each participant spent three seconds viewing each im-



Line Real human ldquoHLArdquo-generatedtype line lines

H

HB

B

3B

6B

8B

Table 2 Texture only line examples comparison of hand-drawn lines with synthesized lines (squiggle D parameterset to zero)

age and then selecting true if they thought the drawing washand-made false if they thought the drawing was computer-generated The timing was chosen empirically such that par-ticipants had enough time to compare drawings without hav-ing too much time to over-analyzing their decision

Overall participants made mistakes in 42 of the decisionsOut of these in 695 of the decisions people selected im-ages to be of hand-drawn type (when they were actuallycomputer-generated) and in 305 of the wrong choices par-ticipant selected images to be of computer-generated type(when they were actually real) A paired T-test showed asignificant difference between computer-generated lines mis-taken for real hand-drawn lines and the real hand drawnlines mistaken for computer-generated lines (paired t(11) =2849 p lt 05) Our experimental results shows that theHLA generated line drawings were good enough to pass forhand-drawn lines

6 Applications

Our technique is suitable for several different domains Ourline generation incorporates easily into existing graphics pro-grams that produce line end points and are editable by artistsor additional algorithms Our technique only requires the se-lection of a pencil type and specification of the end points ofeach line For example Figure 6 show a Gosperrsquos Flowsnake[Gar76] space filling curves rendered with human like linesusing the B pencil setting The synthesized lines are appliedto each of the short individual line segments of each curveThe drawings could be improved by detecting co-linear seg-ments and processing them as a longer line to better emulatea humans artist

In Figure 7 hatching lines are generated and replaced withsynthesized lines in this way we can simulate the effect offilling a part of the plane with unique human-like lines

Figure 6 Flowsnake space filling curve using a B pencilgenerated by HLA

Figure 7 Line hatching generated by HLA (6B pencil)

In Figure 8 a hand drawn illustration is used to extract vectorlines then used as input to the ldquoHLArdquo algorithm resulting inFigure 8(c) We note that our result is quite different than theartistrsquos lines but point out that our result in Figure 8(c) hasthe appearance of being hand-drawn

In Figure 9 an architectural model has been rendered usingour lines This example shows the variability of the line pathsand provides the appearance of an hand-made image

Finally by capturing (e g through tablets) or tracing thelines drawn by artists we can apply the pencil style to thosedrawings as well

7 Conclusion and Future Work

The main contribution of this work is to provide a sys-tem that will serve as a high quality pencil line reproduc-tion agent Our system creates aesthetically pleasing humandrawn pencil lines by using an image synthesis methodand human arm movement replication model The algorithmavoids computationally expensive techniques and large stor-age space More investigation is needed to mimic the appear-ance of variable pressure along the lines Choosing param-eters that will make the lines appear to have more charac-ter will definitely increase the aesthetic property of the lines



(a) Hand Drawn Illustration (b) Input Vector Lines

(c) Result generated by HLA

Figure 8 Our method takes as input lines specified onlyvia their start and end points such as the vector line draw-ing in (b) and produces a line drawing that mimics a realhand drawn graphite pencil drawing (a) by modeling humanarm trajectory and a statistical model of graphite texture toproduce unique aesthetically pleasing and natural lookinglines without the need for databases of example strokes

Similar approaches to the work documented here may workfor drawing curved lines (Figure 10) but further investiga-tion would be necessary to correctly mimic the resultinggraphite textures on curved paths Also more work can bedone to mimic human hatching by conducting similar studiesand observing the relationship between arm and wrist move-ment when producing shorter strokes Figure 7 demonstratesour first attempt to consider hatching as an application moreexperiments and analysis are needed to present accurate hu-man hatching techniques

8 Acknowledgments

We would like to thank all the University of Victoriarsquos graph-ics lab members for their support ideas and help during theprocess of this paper A special thanks to Pertra Neumannfor her statistical skills and everyone who also contributedto our work one way or another We would also like to thankthe reviewers for their helpful comments This research is

Figure 9 A barn generated by HLA (H pencil)

Figure 10 Example curve generated by HLA using Flashand Hogan curve optimization methods inspired by [FS94]

partially supported by the Natural Sciences and EngineeringResearch Council of Canada

References

[Ada71] ADAMS J A Closed-Loop Theory of Motor Be-havior Journal of Motor Behavior 3 (1971) 111ndash149

[Ber67] BERNSTEIN N The Co-ordination and Regula-tion of Movements Pergamon Press London 1967

[BMIG91] BIZZI E MUSSA-IVALDI F GISZTER SComputations Underlying the Execution of Movement ABiological Perspective Science 253 5017 (1991) 287ndash291

[Bre65] BRESENHAM J Algorithm for Computer Con-trol of a Digital Plotter IBM Systems Journal 4 1 (1965)25ndash30

[Bru06] BRUNN M Curve Synthesis by Example Mas-terrsquos thesis University of Calgary 2006

[CRT01] COPELAND A C RAVICHANDRAN G



TRIVEDI M M Texture Synthesis Using Gray-level Co-occurrence Models Algorithms Experimental Analysisand Psychophysical Support Optical Engineering 40 11(Nov 2001) 2655ndash2673

[CVGB97] CONTRERAS-VIDAL J GROSSBERG SBULLOCK D A Neuronal Model of Cerebellar Learningfor Arm Movement Control Cortico-Spino-CerebellarDynamics Learning and Memory 3 6 (MarApr 1997)475ndash502

[FH85] FLASH T HOGAN N The Coordination of ArmMovements An Experimentally Confirmed MathematicalModel The Journal of Neuroscience 5 7 (1985) 1688ndash1703

[FS94] FINKELSTEIN A SALESIN D H Multiresolu-tion Curves In Proc SIGGRAPH (New York 1994)ACM Press pp 261ndash268

[FTP03] FREEMAN W T TENENBAUM J B PASZTOR

E C Learning Style Translation for the Lines of a Draw-ing ACM Transactions on Graphics 22 1 (Jan 2003)33ndash46

[Gar76] GARDNER M Mathematical Games In WhichldquoMonsterrdquo Curves Force Redefinition of the WordldquoCurverdquo Scientific American 235 (Dec 1976) 124ndash133

[GM85] GAGALOWICZ A MA S D Sequential Synthe-sis of Natural Textures Computer Vision Graphics andImage Processing 30 3 (June 1985) 289ndash315

[HL94] HSU S C LEE I H H Drawing and AnimationUsing Skeletal Strokes In Proc SIGGRAPH (New York1994) ACM Press pp 109ndash118

[JB87] JULESZ B BERGEN R Textons The Fundamen-tal Elements in Preattentive Vision and Perception of Tex-tures In Readings in Computer Vision (San Francisco1987) Morgan Kaufmann Publishers Inc pp 243ndash256

[JEGPO02] JODOIN P-M EPSTEIN E GRANGER-PICHEacute M OSTROMOUKHOV V Hatching by Examplea Statistical Approach In Proc NPAR (New York 2002)ACM Press pp 29ndash36

[KG92] KAWATO M GOMI H The Cerebellum andVOROKR Learning Models Trends in Neurosciences 1511 (Nov 1992) 445ndash453

[KMMlowast02] KALNINS R D MARKOSIAN L MEIER

B J KOWALSKI M A LEE J C DAVIDSON P LWEBB M HUGHES J F FINKELSTEIN A WYSI-WYG NPR Drawing Strokes Directly on 3D ModelsACM Transactions on Graphics 21 3 (July 2002) 755ndash762

[MAJ91] MAZZONI P ANDERSEN R JORDAN M AMore Biologically Plausible Learning Rule for NeuralNetworks Proceedings of the National Academy of Sci-ences 88 10 (May 1991) 4433ndash4437

[MIAlowast08] MACIEJEWSKI R ISENBERG T ANDREWS

W M EBERT D S SOUSA M C CHEN W Mea-suring Stipple Aesthetics in Hand-Drawn and Computer-Generated Images Computer Graphics amp Applications28 2 (MarApr 2008) 62ndash74

[Pla95] PLAMONDON R A Kinematic Theory of RapidHuman Movements Parts I and II Biological Cybernetics72 4 (Mar 1995) 295ndash307 309ndash320

[SABS94] SALISBURY M P ANDERSON S EBARZEL R SALESIN D H Interactive Pen-and-Ink Il-lustration In Proc SIGGRAPH (New York 1994) ACMPress pp 101ndash108

[SAGlowast05] SHIRLEY P ASHIKHMIN M GLEICHER MMARSCHNER S REINHARD E SUNG K THOMPSON

W WILLEMSEN P Fundamentals of Computer Graph-ics 2nd ed A K Peters Ltd Natick MA 2005

[SB99] SOUSA M C BUCHANAN J W Computer-Generated Graphite Pencil Rendering of 3D PolygonalModels Computer Graphics Forum 18 3 (Sept 1999)195ndash207

[SB00] SOUSA M C BUCHANAN J W ObservationalModel of Graphite Pencil Materials Computer GraphicsForum 19 1 (2000) 27ndash49

[Sch75] SCHMIDT R A A Schema Theory of DiscreteMotor Skill Learning Psychological Review 82 4 (July1975)

[SD04] SIMHON S DUDEK G Sketch Interpretationand Refinement Using Statistical Models In Render-ing Techniques (Aire-la-Ville Switzerland 2004) EURO-GRAPHICS Association pp 23ndash32

[SS02] STROTHOTTE T SCHLECHTWEG S Non-Photorealistic Computer Graphics Modeling Animationand Rendering Morgan Kaufmann Publishers San Fran-cisco 2002

[SSRL96] SCHUMANN J STROTHOTTE T RAAB ALASER S Assessing the Effect of Non-photorealisticRendered Images in CAD In Proc CHI (New York1996) ACM Press pp 35ndash42

[SSSS98] SCHLECHTWEG S SCHOumlNWAumlLDER BSCHUMANN L STROTHOTTE T Surfaces to LinesRendering Rich Line Drawings In Proc WSCG (1998)vol 2 pp 354ndash361

[UKS89] UNO Y KAWATO M SUZUKI R Forma-tion and Control of Optimal Trajectory in Human Multi-joint Arm Movement Biological Cybernetics 61 2 (June1989) 89ndash101

[Woo99] WOODWORTH R The Accuracy of VoluntaryMovement Psychological Review Monograph Supple-ments 3 (1899) 1ndash114

[ZG01] ZALESNY A GOOL L V A Compact Modelfor Viewpoint Dependant Texture Synthsis In ProcSMILE (Berlin 2001) vol 2018 of LNCS Springer-Verlag pp 124ndash143



plication may use this algorithm to produce lines that resem-ble human-drawn lines (e g [Bre65]) Ideally such an algo-rithm would reproduce the details carried by a human-drawnpencil line without the need of storage libraries or user inputto specify line attributes (as is the case e g with [SB00])Since the lines do not have set widths colors or particulartextures our proposed method will approximately reproducethe pencil details within the stroke it follows In additionsuch a line should not have repeated sections so that eachline is unique

We divide our algorithm for generating a human-like pen-cil lines into two parts (a) synthesizing the path that cor-responds to human arm movement and (b) synthesizing thepencil texture applied along the path inspired by the texturesproduced by real pencils Based on this general approachour algorithm

bull produces high quality simulations of hand-drawn lines

bull easily incorporates into existing applications

bull produces lines of arbitrary length

bull does not require a library of sample lines (only a grey leveldynamic range array and a co-occurrence matrix) and

bull creates unique lines for every set of input point pairs

Our contribution is a high quality pencil media line reproduc-tion agent for creating aesthetically pleasing lines that mimichuman-drawn lines For this purpose we use methods of im-age synthesis and a model of human arm movement for itsreplication Our method avoids computationally expensivetechniques and large storage space while continuously pro-ducing new unique lines

2 Previous Work

Our work draws from research on interactive non-photorealistic rendering (NPR) methods that approximateartistic hand-drawn images or paintings In particular wedraw from NPR approaches for generating human line draw-ings and the simulation of graphite pencil texture texturesynthesis and literature on the trajectory of human armmovement while drawing

21 Human Line Drawings

Characteristics of lines in sketched and hand-drawn imageshave been studied closely in the field of NPR Many algo-rithms captured style characteristics and applied multipleparameters (such as length width pressure etc) Previousmethods [HL94 SSSS98 SS02] used style parameters anddistorted a textured predefined piecewise polynomial curveor polygon path to create a stylized line Other approachesreproduced and synthesized similar styles from exampleslines [JEGPO02 KMMlowast02 FTP03 FS94 SD04 Bru06]

Our method differs from example-based methods in that we

do not require example lines to generate unique paths andtextures For each pencil type there exists two pieces ofinformation in the algorithm a dynamic range and a co-occurrence matrix We simply require vector endpoints toproduce lines Our aim is not to generate varying style froma single given style as seen in example-based methods butto develop an algorithm that generates lines that vary in pathorientation and texture synthesis mimicking observed realhuman movement and graphite pencil deposits noticed fromstraight line drawings on paper

Our work is inspired by methods that simulated pencils asa medium specifically the work by Sousa and Buchanan[SB99 SB00] who contributed a low-level simulation ofgraphite pencils on a paper medium They designed a sys-tem to simulate graphite pencil on paper using microscopicobservations [SB99] Their work focused on generating fillstrokes using it for hatching purposes to reproduce artisticdrawings Our method differs because our lines are not re-stricted to short strokes and can vary greatly in length withno repeating segments We also base our work on interac-tive pen-and-ink illustration by Salisbury et al [SABS94]who described a level-of-detail system that interactively pro-duces multiples of strokes to avoid tediously placing themmanually Our algorithm for drawing lines could easily beincorporated into the above approaches adding the benefitof a model of human arm movement and a simple perceptualsimulation model for graphite pencils without the require-ment of a library of lines to copy paste and reshape

22 Texture Synthesis

We were also influenced by a texture synthesis method basedupon sequential synthesis [GM85] that preserves the sec-ond order statistics of the natural texture into the newlysynthesized texture Gagalowicz and Ma [GM85] also pro-vided experimental evidence that the visual system is onlysensitive to second-order spatial averages of a given tex-ture field More recent texture synthesis research renamessecond order statistics (spatial averages) using the term co-occurrence (CC) models or grey level co-occurrence (GLC)models [CRT01] We use the GLC model which is definedas the proportion of second order probability statistics ofgrey levels pairs that differ by a delta in the texture

Applying co-occurrence matrices to synthesize texture havebeen shown to be extremely efficient in creating artificialtextures that are hard to discriminate from in real textures[JB87] the amount of data necessary to achieve the synthesisis very small and the texture can be generated easily to fit allkinds of surface shapes and sizes Using this method allowsus to control the second-order spatial averages of a given tex-ture since the synthesis is achieved directly from them with-out the computation of higher order statistics [GM85] Alsotexture similarity techniques using GLC probability distribu-tion have shown to have high correlation with human percep-














3 Algorithm


31 The Path





5minus10τ3)


5minus10τ3) (1)

where

τ =tt f

t isin [02]





(a) Short line

(b) Medium Line

(c) Long line








(200400] 03gt 400 02


32 The Texture







C(i j) =n

sump=1

m

sump=1

1 i f I(pq) = i and


(2)






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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37


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5 Verification





H

HB

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3B

6B

8B




6 Applications
















8 Acknowledgments





References





















































3 Algorithm


31 The Path





5minus10τ3)


5minus10τ3) (1)

where

τ =tt f

t isin [02]





(a) Short line

(b) Medium Line

(c) Long line








(200400] 03gt 400 02


32 The Texture







C(i j) =n

sump=1

m

sump=1

1 i f I(pq) = i and


(2)






0

50

100

150

200

250

300

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37


Gre

y L

evel

Inte

nsi

ties








4 Results








5 Verification





H

HB

B

3B

6B

8B




6 Applications
















8 Acknowledgments





References










































(a) Short line

(b) Medium Line

(c) Long line








(200400] 03gt 400 02


32 The Texture







C(i j) =n

sump=1

m

sump=1

1 i f I(pq) = i and


(2)






0

50

100

150

200

250

300

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37


Gre

y L

evel

Inte

nsi

ties








4 Results








5 Verification





H

HB

B

3B

6B

8B




6 Applications
















8 Acknowledgments





References












































0

50

100

150

200

250

300

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37


Gre

y L

evel

Inte

nsi

ties








4 Results








5 Verification





H

HB

B

3B

6B

8B




6 Applications
















8 Acknowledgments





References











































H

HB

B

3B

6B

8B




6 Applications
















8 Acknowledgments





References














































8 Acknowledgments





References









































mimicking hand-drawn pencil lines · z. meraj & b. wyvill & t. isenberg & a. gooch...

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