Animating cartoon faces by multi-view drawings

COMPUTER ANIMATION AND VIRTUAL WORLDSComp. Anim. Virtual Worlds 2010; 21: 193–201Published online 27 May 2010 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/cav.344...........................................................................................Animating cartoon faces by multi-viewdrawings

By Xiang Li, Jun Xu, Yangchun Ren and Weidong Geng*

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In this paper, we present a novel framework for creating cartoon facial animation frommulti-view hand-drawn sketches. The input sketches are first employed to construct a basemesh model by using a hybrid sketch-based method. The model is then deformed for each keyviewpoint, yielding a set of models that closely match the corresponding sketches. Weintroduce a view-dependent facial expression space defined by the key viewpoints and thebasic emotions to generate various facial expressions viewed from arbitrary angles. Theoutput facial animation conforms to the input sketches and maintains frame-to-framecorrespondence. We demonstrate the potential of our approach through an easy-to-usesystem, where the animating of cartoon faces is automated once the user accomplishessketching and configuration. Copyright © 2010 John Wiley & Sons, Ltd.

KEY WORDS: facial animation; cartoon animation; sketch-based interfaces for modeling;view-dependent geometry; non-photorealistic rendering

Introduction

Facial expression plays one of the most substantial rolesin depicting human characters in cartoon animation.Because of the complexity of human facial anatomy andthe subtlety of facial motions, a large amount of manualwork is invested in drawing facial expressions, especiallywhen the characters have to be seen from multiple view-points. Although a number of research tools1–3 are builtto facilitate the creation of stylized facial animation, mostof these tools describe characters as 2D representations,making it difficult to generate faces with correct occlu-sions and distortions when the cameras rotate. One mightattempt to remedy this problem using non-photorealisticrendering techniques4 by creating a 3D geometrical modeland generating cartoon renderings. However, theseapproaches involve extensive modeling of 3D faces andusually reduce the vividness of 2D animation.

Notice that animators create facial animations startingfrom the multi-view sketches drawn on the model sheet,with their prior knowledge of the 3D characters. Our

*Correspondence to: W. Geng, State Key Laboratory ofCAD&CG, Zhejiang University, Hangzhou 310027, China.E-mail: [email protected]

first fundamental idea is to create a 3D model from themulti-view sketches, which represents the mental modelexplicitly. To eliminate the effort required in the model-ing, we construct the model using a hybrid sketch-basedmethod5 that exploits domain-specific knowledge.

However, a lively cartoon character often has a view-specific appearance (e.g., the ears of Disney’s MickeyMouse4), which cannot be captured with a conventional3D model. To inject view-dependent stylization intofacial animation, our second fundamental idea is that themesh model is deformed to fit the input sketches for eachviewpoint. We adapt and improve the techniques of view-dependent geometry6–8 by applying a detail-preservingdeformation method that automates the correspondencespecification.

Uniquely combining view-dependent animation withfacial expressions, we introduce a view-dependent facialexpression space defined by the key viewpoints andthe basic emotions. For each basic emotion, a view-dependent model is constructed from the multi-viewsketches which are semi-automatically created. The out-put facial animation generated by blending in the facialexpression space conforms to the hand-drawn expres-sions and possesses frame-to-frame correspondence. Themain contributions of this paper are:

............................................................................................Copyright © 2010 John Wiley & Sons, Ltd.

X. LI ET AL............................................................................................

Figure 1. Our system takes as input a set of multi-view sketches (a) (the half side view is zoomed in), and a set of reconstructionrules (b). The base model (c) reconstructed from the sketches is deformed to create a view-dependent model (d). Given an arbitrary

viewpoint, we generate a view-specific face (e), which can be used to produce stylized facial expressions (f).

� We provide a sketch-based approach to create car-toon facial animation. The generated faces, whichclosely conform to the multi-view sketches, are ani-mated depending on both the viewpoints and theemotions.

� The techniques of view-dependent geometry areimproved by introducing an automatic detail-preserving deformation algorithm, as well as a hybridsketch-based method to construct the base models.

� An extendable facial expression space defined by thekey viewpoints and the basic emotions is introduced.A curve deformation algorithm is applied to facilitatethe construction of novel basic expressions.

Related Work

Conventional computer animation techniques canhardly be used to generate cartoon faces as the shapesand the motions usually go together in the drawings.Ruttkay and Noot presented an interactive system to ani-mate 2D cartoon faces, separating the appearances andthe behaviors1. Li et al. animated face cartoons based onemotions derived from speech signal2. These methodsmostly deal with the transformations in a plane parallelto the drawing canvas. To achieve the transformationsoutside the plane, Di Fiore et al. proposed 2.5D modelingand animation techniques3,9,10. Since the depths of theinterpolated components were determined by the draw-ing order stored in the 2D extreme frames, the resultmay suffer from improper handling of half-occlusion andorder-reversal.

Recently, non-photorealistic rendering (NPR) techniquesare widely used to generate stylized animation startingfrom 3D models4. These approaches can render objectswith various artistic styles and correct occlusions. How-ever, extensive modeling and animation of objects arerequired and the underlying 3D shapes are rendered tooinflexible9.

Some researchers adapted sketch-based interfaces formodeling (SBIM)5,11 to facilitate the creation of cartoonanimation by eliminating the modeling effort. Di Fioreand Van Reeth employed approximate 3D models toretain proportions and ensure coherence12. Chen et al.constructed a set of consistent 3D models to generatecharacter animation13. Since they constructed the modelsfrom the single-view sketches, changing the viewpointdrastically will cause some errors due to the lack of infor-mation from other viewpoints.

To inject the view-dependent stylization into com-puter animation, Rademacher presented a techniquecalled view-dependent geometry (VDG)6, wherein the 3Dbase model changes shape based on the viewpoint. Liet al. introduced a view-dependent deformation tech-nique that allowed a smaller amount of intervention7.Chaudhuri et al. presented semi-automatic posing anddeformation algorithms8 and provided a framework forrepresenting view-dependent animation by introducingthe concept of a view space14. These methods mostlyfocus on articulated character animation and can hardlybe used in the context of facial animation due to thecomplexity of faces. In this paper, we bridge the gapby combining VDG with the facial expression space15, andminimize user interaction.

Overview

In traditional animation, animators often depict theappearance of a character by a set of multi-view sketches.Our system pipeline shown in Figure 1 starts with thesketches and a set of reconstruction rules. The sketches areinput by a pen-based interface, each of which is com-prised of a set of strokes. The user draws a sequenceof points to represent a stroke and gives it a meaning-ful label. The character is drawn under the guidance ofthe projection of its bounding box, which automates thealignment of the multi-view sketches.

............................................................................................Copyright © 2010 John Wiley & Sons, Ltd. 194 Comp. Anim. Virtual Worlds 2010; 21: 193–201

DOI: 10.1002/cav

ANIMATING CARTOON FACES...........................................................................................

Figure 2. Base model construction. (a) The front and side view silhouettes are used to construct a rough skull mesh. (b) The roughmesh (solid black line) is deformed to fit the silhouettes by constraining one vertex to each sample ray (grey arrow). (c) The interior

vertices of the feature mesh are computed by subdivision while the boundary vertices converge to the boundary curve.

To create 3D models, we apply a constructive methodthat exploits a template of reconstruction rules to repre-sent domain-specific knowledge. The template providesthe default parameters of sketch interpretation for eachbasic component. The sketch interpretation rules implywhich strokes are corresponding to a component andhow to construct the model from the sample points. Allthe components are organized into a tree construction.When the user creates a new character, he/she loads aproper template, edits the default parameters and cre-ates additional components. The edited template can besaved for reuse.

After sketching and rules configuration, stylized facialexpressions seen from various viewpoints are gener-ated accordingly. The system pipeline proceeds in threestages: base model construction, view-dependent defor-mation, and facial expression generation. In the firststage, a base mesh model is created from the multi-viewsketches. In the second stage, we construct a view-dependent model that includes a set of key deformationsmatching the input sketches. Finally, a facial expressionspace formed by the key viewpoints and the basic emo-tions is introduced to generate facial animation.

Base Model Construction

The goal of the first stage is to efficiently construct smoothmeshes that fit the sketches. Since creating a complexmodel by employing the WIMP (Window, Icon, Menu,Pointer) paradigm requires considerable expertise andeffort5, the algorithm we use is a sketch-based model-ing method. The face model is divided into diverse basiccomponents according to the reconstruction rules. For

each component, we obtain the corresponding strokesby searching for the labels configured in the template ofrules. The model construction method, which consistsof two independent algorithms for closed surfaces andsurfaces with boundaries, is used to create the skull andthe facial features separately. The components are thenbrought together as a complex face model (see Figure 2).

Closed Surface

The skull is modeled as a closed surface, which can beapproximately created by an inflation algorithm11. Sinceit is difficult to reconstruct the face shape from a sin-gle sketch, we employ the front and side view sketchesto create a rough mesh. The silhouettes extracted fromthese sketches are sampled to generate the initial vertices,according to the sampling interval configured in the tem-plate. Then the initial vertices are converted into quarterovals and the polygonal mesh is generated accordingly(see Figure 2a).

The rough mesh is plausible when seen from the anglesclose to the front and side views, but it is probable that themesh disagrees with the other input sketches. Inspiredby the surface optimization techniques16,17, we employ themulti-view silhouettes to fit the surface (see Figure 2b).The silhouettes are automatically aligned with the roughmesh Vr by matching the bounding boxes. The roughmesh is converted to differential coordinates18 with L =�uVr , where �u is the uniformly discretized Laplacianmatrix. The new mesh Vs is then computed by iterativelysolving the least-squares system:

arg minVs

(‖�uVs − wlL‖2 + wc‖CmVs − Pm‖2) (1)


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where Pm contains the target positions and Cm storesthe vertices to be constrained. We use the method givenin Reference17 to determine Pm and Cm, whether or notthe sampled rays intersect the shape. The weight wl isincreased from 0 to 1 during the iterations to ensuredetail preservation, and wc determines how closely themesh matches the silhouettes. Since soft constraints areused to balance fitting against smoothness, the algorithmachieves our goal and is robust to inaccurate user inputs.

Surface with Boundaries

Most of the facial features can be taken as surfaces withboundaries. For such a component, we first generatethe boundary curve by getting the 2D positions of theboundary vertices from the front view, and the depthof these vertices from the side view. If a component isoccluded when seen from the side view, the depth isobtained from its symmetrical component. The combinedsubdivision scheme19 then generates the mesh taking intoaccount boundary conditions. As the subdivision iter-ations progress, the interior vertices are computed bythe modified butterfly scheme while the boundary verticesconverge to the boundary curve (see Figure 2c). Thisscheme ensures the surface to be smooth and consistentwith the input strokes. After subdivision, the positions ofthe surface vertices can be adjusted along the projectiondirection, making it possible to attach the surface to itsparent component.

For the facial features that cannot be reconstructedfrom the boundary curves (see the nose in Figure 5d),we also apply an extrusion algorithm using base ringsand extruding strokes11. Besides, a sketching tool basedon the technique of cluster polygons20 is provided to createcomplex cartoon hair (see Figures 1f and 5b, e).

View-dependentDeformation

After creating the 3D base model, we fit the model tothe input sketches depending on the viewpoints. To thisend, a view-dependent model (VDM) is constructed, whichconsists of the base model, a set of key deformations andkey viewpoints6. We provide an intuitive way of definingthe viewpoint by converting it to geographic coordinates(see Figure 3a). A viewpoint is defined in terms of itslatitude ϕ, longitude λ and height h in this coordinatesystem. The user can set the key viewpoints by adjustingthe key angles and the key heights.

Key Deformation

It is important to provide a deformation method thatmakes the model closely match the input sketch froma key viewpoint. An intuitive choice appears to bethe surface optimization algorithm used for fitting thebase model. Unfortunately, we have encountered ashortcoming inherent to this algorithm, which is due tothe complexity of facial characteristics. When drawinga face from multiple angles, animators will remarkablymodify the positions and shapes of the features, makingit ambiguous to find the correspondences. Furthermore,the modifications will rotate the meshes while differen-tial coordinates are not rotation-invariant. We remedythis problem by coupling affine transformation withsurface optimization. For each component, we align thebase mesh with the corresponding strokes by applyinga combined affine transformation, to ensure that theirendpoints coincide (see Figure 3c). After that, differentialcoordinates L are generated with L = �uVf , whereVf represents the transformed base mesh. The keydeformation Vk is then computed by iteratively solving

arg minVk

(‖�uVk − wlL‖2 + wc‖CvVk − Pv‖2) (2)

For a surface with boundaries, Cv is filled with theboundary vertices to be constrained, and the strokes aresampled to determine the target points Pv whose prin-cipal normals pass through the boundary vertices (seeFigure 3d). For a closed surface, the method based on thesampled rays from the current viewpoint17 is used. Sinceour method achieves the automatic correspondence,the user interaction is no more than the specification ofthe stroke labels while sketching, which is better thanmarking a lot of corresponding points7,8.

With the 3D construction, we can deal with theoccluded components. If a component is completelyoccluded, we insert a dummy deformation simply copy-ing the base model6. On the other hand, the half-occludedcomponents are deformed properly by constraining thenon-occluded vertices and smoothly interpolating theoccluded vertices with the Laplacian coordinates. Ourmethod ensures that the topology does not change dur-ing the deformation.

View-dependent Animation

The key deformations can be blended to generate a view-specific face for a given view direction (the view distancewill be discussed as below). Generally, a viewpoint is sur-


DOI: 10.1002/cav


Figure 3. View-dependent deformation. (a) A view direction is defined in terms of its latitude ϕ and longitude λ in our geographiccoordinate system. (b) The bilinear interpolation scheme is used to blend the key deformations. (c) The surface with boundaries(black) is first transformed to align with the corresponding strokes (gray). (d) The transformed surface is deformed to closely match

the strokes.

rounded by two key latitudes and two key longitudes(represented as ϕ0

k , ϕ1k , λ0

k and λ1k) and the four closest

key viewpoints can be found at their intersections (seeFigure 3b). We employ a bilinear interpolation scheme toblend the key deformations. The blending coefficients arecomputed from the latitude and the longitude separately.For a given viewpoint νt = (ϕt, λt) and the associated keyviewpoints ν

i,j

k = (ϕik, λ

j

k), the latitude weight wiϕ is given

by the barycentric coordinates of ϕt with respect to ϕ0k and

ϕ1k . The longitude weight wj

λ is computed in the same way.These weights are continuous as the camera is movedwithin and cross the areas surrounded by the key view-points. Since the key deformations share the same vertexconnectivity, the resulting mesh is given as

Vt =∑

i,j

wiϕw

j

λVi,j

k

where Vi,j

k represents the key deformation correspond-ing to ν

i,j

k .

To deal with the condition that the character changesshape based on the distance, we can analogously intro-duce wh and interpolate the key deformations with theclosest heights. Note that the key deformations are inde-pendent of the camera path. Given a camera path, acontinuous view-dependent animation is generated byinterpolating the key deformations, even when the cam-era exceeds the range covered by the input sketches.

Facial ExpressionGeneration

In this section, we inject emotions into VDMs togenerate stylized facial expressions. Although manymethods have been explored to synthesize emotionalanimation15,21, little attention has been paid to view-specific expression. In this paper, we introduce aview-dependent facial expression space (VDFES) that con-


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X. LI ET AL............................................................................................

Figure 4. View-dependent facial expression space. (a) The space is built by the basic emotions and the key viewpoints, in whichthe face can be animated depending on both the performance and the camera. (b) The shape changes of a basic expression fromthe front and side views are captured and retargeted onto the sketches from a novel view (solid). The generated sketches can be

fine-tuned by the user (dashed).

sists of the key viewpoints and the basic emotions (seeFigure 4a). The default basic emotions comprise joy,sadness, fear, disgust, surprise, and anger15. Each basicemotion is mapped to a VDM that depicts a basic facialexpression. These VDMs with different emotions allowthe generation of facial animation specific to a given emo-tion sequence and a camera path.

Facial Expression Space

The VDFES is constructed from sets of multi-viewsketches depicting the basic emotions. It is tedious forthe user to manually draw the basic expressions fromall the key viewpoints. To eliminate this time consum-ing process, we capture the shape changes of each basicexpression from the front and side views, and retar-get them onto the sketches from other viewpoints (seeFigure 4b). The algorithm starts with the neutral expres-sion that is manually drawn from all the key viewpoints.For each basic emotion, we sample the correspondingfront and side view sketches input by the user to con-struct a set of 3D control curves. Given a key viewpoint,the control curves are projected onto the drawing planeto deform the neutral expression. The deformation algo-rithm is a combination of 2D affine transformation andcurve optimization. Let er be the given basic emotion anden be the neutral one. For each stroke Vl, we write Pr

and Pn as the projected control curves with respect to

er and en respectively. We first match the endpoints ofVl, Pr and Pn together by applying 2D affine deforma-tions. Then the expression offset is added to Vl to formpositional constraints Pd = Vl + Pr − Pn. The resultingstroke is then computed by solving

arg minVd

(‖�sVd − wlL‖2 + wc‖CdVd − Pd‖2) (3)

where Cd represents the correspondences between Vl

and Pd . The differential coordinates are generated withL = �sVl, where �s is the second order differentialmatrix. The user can fine-tune the generated sketches toenhance the artistic style.

When the required sketches have been generated,the VDFES is accordingly constructed. For each basicemotion, an independent base model and a set ofkey deformations are created from the correspondingsketches, by using the algorithms described in previoussections. The VDFES can be expanded easily by insertingnew basic emotions or key viewpoints.

Facial Expression Animation

The VDFES can be used for the synthesis of variousexpressions viewed from arbitrary angles. We employblendshape interpolation method21 to generate novel facialexpressions by blending the basic expressions. Given anemotion sequence and a camera path, a view-dependent


DOI: 10.1002/cav


animation with facial expression is generated (see Algo-rithm 1). The emotion sequence can be specified by theuser by applying interactive tools that explore the facialexpression space15, or extracted from a video sequenceby tracking and recognition.

Since the algorithm ensures that the generated facemodels maintain vertexwise correspondences, we canrender the models using various NPR techniques andgenerate smooth animation sequences. In our system,an outline detection algorithm is used to generate sil-houettes and boundaries4, and the techniques of texturemapping and cartoon shading are applied to simulatevarious cartoon styles that are time consuming to createin 2D animation.

Results

Our approach has been tested on the characters of vari-ous types. Figure 5(a) shows the comparative snapshotsof the facial expressions of “Cao E”, which are created byusing the sketches from six key viewpoints (see Figure 1a)with six basic emotions. Figure 5(b–e) shows some morecomplex face models and facial expressions generated inour system. The resulting facial expressions reproducethe artistic styles of the input sketches from various view-points, and have similar effects to those in traditionalanimation.

Algorithm 1 Facial expression generation.

Require: The VDFES must have beencreated.

Require: An emotion sequence must beinput.

Require: A camera path must be tracedout.

1: n ← number of frames2: for i = 1 to n do3: et ← ist emotion in the sequence4: νt ← ist viewpoint on the camera

path5: e1..m

r ← m related basic emotionsof et

6: ν1..lk ← l key viewpointssurrounding νt

7: for j = 1 to m do

8: Vjt ← interpolate the l key

deformations of ejr

9: end for10: Ve ← blend the basic

expressions V1..mt

11: Render Ve

12: end for

Our current implementation is written in C++ run-ning on the Windows platform and the TAUCS library

Figure 5. Experimental results. (a) In this comparative experiment, we recreate the facial expressions in the traditional animationclip (left) by using the VDMs constructed in this paper (right). (b–e) For each test character, we constructs the key deformations(middle) that closely match the input sketches (left), and generates the stylized facial expressions from various viewpoints (right).


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is used for the computation of sparse linear systems. Wetested the system on a 3.0 GHZ Intel Core 2 Duo with3.25 GB of RAM. The sketches were input with Intuos3 from WACOM. For the example of “Cao E”, the 36sketches took a trained user about 2 hours to create,and the template configuration required 10 minutes. Inthe modeling stage, creating a base model (914 vertices)took 1.1 seconds and view-dependent deformation ranat 0.29 seconds per viewpoint, making the total process-ing time 17 seconds to construct the VDFES. To createthe facial animation sequence (208 frames) shown in Fig-ure 5(a), the user spent about 15 minutes to specify thecamera path and the emotion sequence. The animatingtook 0.10 seconds per frame, or 21 seconds for the wholesequence. As a contrast, it took an animator 6 hours tocreate the same facial expressions using traditional key-frame approach. The experimental results show that ourapproach greatly reduces the animation workload andaccordingly speeds up the production of cartoon facialanimation.

Conclusions

We have introduced a sketch-based framework for thecreation of cartoon facial animation. A hybrid sketch-based method is introduced to construct the face modeland an automatic view-dependent deformation algo-rithm is presented to stylize the appearance. In the areaof NPR, deforming and rendering 3D models to simulatehand-drawn styles have been widely concerned4. On theother hand, SBIM provide an inverse process to recon-struct 3D shapes from hand-drawn strokes5. We bridgethe gap between these two parallel research directions,capturing the shape cues from finite multi-view sketchesand retargeting them onto arbitrary views. Our approachessentially solves the occlusion problems of the 2D/2.5Danimation systems1,3,10, and improves the techniques ofVDG6–8,14 by reducing the user workload. We applythese ideas to the context of facial animation by combin-ing facial expressions with view-dependent animation,which could also be coupled with the skeleton-basedapproaches7,8 to facilitate the creation of articulated ani-mation.

One limitation of our technique is that the expres-siveness of the 3D modeling scheme is limited by theflexibility of the reconstruction rules, making it difficultto construct freaky face models. An evocative methodcould help to remedy this problem5. Another limitationis that the stroke labels are specified manually by the userand the interaction process is still tedious. This process

could be automated by an example-based recognitionmethod. Finally, the hair movement, which is also impor-tant for a cartoon face, is not mentioned. We are currentlyinvestigating the incorporation of kinematics to animatecartoon hair.

ACKNOWLEDGEMENTS

This work was partly supported by NSFC 60633070 and60773183, National 863 High-Tech Program (Grant no:2006AA01Z313 and 2006AA01Z335), and National Key Tech-nology R&D Program of China (Grant no: 2007BAH11B02 and2007BAH11B03). It is also supported by NCET-07-0743 andPCSIRT 0652.

References

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3. Di Fiore F, Van Reeth F. Multi-level performance-drivenstylised facial animation. In Proceedings of Computer Anima-tion and Social Agents, 2005.

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Authors’ biographies

Xiang Li is currently a PhD candidate at the Depart-ment of Computer Science, Zhejiang University, China.He received the BE degree from the Department of Com-puter Science, Zhejiang University, China, in 2006. Hisresearch interests include computer animation, sketch-based interfaces, and non-photorealistic rendering.

Jun Xu is currently a PhD student at the Departmentof Computer Science, Zhejiang University, China, super-vised by Professor Weidong Geng. Her research interestsinclude computer animation and human computer inter-action.

Yangchun Ren is a graduate student from the Depart-ment of Computer Science at Zhejiang University. Hisresearch interests include computer aided cartoon designand computer vision.

Weidong Geng currently is a professor of Collegeof Computer Science, Zhejiang University, China. Hereceived the BSc degree from the Computer ScienceDepartment in Nanjing University, China, in 1989, andan MSc degree from the Computer Science Departmentof National University of Defense Technology in 1992. In1995, he received his PhD from the Computer Science andEngineering Department of Zhejiang University, China.From 1995 to 2000, he was in Zhejiang University, wherehe took charge of a number of projects about CAD/CG,and intelligent systems. He joined Fraunhofer Institutefor Media Communication (former GMD.IMK), Ger-many, as a research scientist in 2000. In 2002, he workedin Multimedia Innovation Center, The Hong Kong Poly-technic University, Hong Kong. Since 2003, he worksin State Key Laboratory of CAD&CG, and his currentresearch focuses on computer aided design, computeranimation, multi-modal interface, interactive media, anddigital entertainment.


DOI: 10.1002/cav

Animating cartoon faces by multi-view drawings

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