Demeulemeester, Aljosha

Hollemeersch, Charles

Pieters, Bart

Samyn, Koen

Van de Walle, Rik

Van Hoecke, Sofie

English

Ghent University Academic Bibliography

CGI2012 manuscript No.(will be inserted by the editor)Real-time Animation Of Human Characters With FuzzyControllersKoen Samyn · Sofie Van Hoecke · Bart Pieters · Charles Hollemeersch ·Aljosha Demeulemeester · Rik van de WalleAbstract The production of animation is a resourceintensive process in game companies. Therefore, tech-niques to synthesize animations have been developed.However, these procedural techniques offer limitedadaptability by animation artists. In order to solve this,a fuzzy neural network model of the animation is pro-posed, where the parameters can be tuned either bymachine learning techniques that use motion capturedata as training data or by the animation artist him-self. This paper illustrates how this real time proceduralanimation system can be developed, taking the humangait on flat terrain and inclined surfaces as example.Currently, the parametric model is capable of synthe-sizing animations for various limb sizes and step sizes.Keywords Procedural Animation · Fuzzy Control ·Real-time1 IntroductionAnimation of human and non-human characters is avery important topic for the production of video games,movies or commercials. With the push for 3D worldsthat are immersive and fully interactive, a large per-centage of production time is spent on the develop-K. SamynGhent University - IBBT, ELIS Department, Ghent, Belgium,E-mail: koen.samyn@ugent.beK. SamynHowest University College, Kortrijk, BelgiumS. Van HoeckeHowest University College, Kortrijk, Belgium,E-mail: sofie.van.hoecke@howest.beS. Van Hoecke · B. Pieters · C. Hollemeersch· A. Demeulemeester · R. van de WalleGhent University - IBBT, ELIS Department, Ghent, Belgiumment of character animation. Game studios commonlyuse handcrafted animations by animation artists or mo-tion capture during production. However, because bothtechniques are very labor intensive, different algorithmsto create procedural animation (both real-time and nonreal-time) have been proposed.While talking to established animation artists andreading the standard work [13] for animation artists, itbecame clear that there is a large disconnect betweenthe procedural animation community and the anima-tion artists community. This disconnect is mainly situ-ated in the conflicting goals of both communities. Theartist community wants to retain full artistic controlover the finalized animations, in other words, in orderto tell a story (be it a game or movie), the artist wantsto express emotion in an animation, and create believ-able characters. Most procedural animations techniqueshowever focus on plausibility and do not provide theanimation artists with the necessary tools to changethe behavior of the synthesized animation. Therefore, itis not surprising then that procedural animation tech-niques face an uphill battle to find acceptance in theanimation artist community.To create procedural animations that better fit theneeds of to the artistic community, it is important towork on the timings and spacings of the animation.Most animation artists use time sheets to define theextreme and in-between poses of the animation, andto refine the handcrafted animations from this startingpoint. Often, an artist will spend several hours to getjust one pose exactly right. The timings and spacingsare also used to create the illusion that an animatedcharacter is a lightweight character, with for examplean energetic walk or a voluminous character, with aslower gait and more arching movements.2 Koen Samyn et al.With these considerations in mind, it was clear thata procedural animation system moves away from black-box solutions and presents the artists with an array ofparameters that have a significant impact on the tim-ings and spacing of the synthesized motions. The sys-tem must also present these parameters in a languagethat is easy to grasp for an animation artist. An otherconsideration was that these same parameters shouldbe defined in a way that enables a machine learning ap-proach. A system with these characteristics can learnfrom handcrafted animations or motion capture data,this way enabling the animation artist to convert non-procedural animations into a procedural version. In do-ing so, the style of the source animation is copied intothe parametric model. The remainder of the paper isas follows. In Section 2 related work is discussed. InSection 3 the architecture of our new approach to pro-cedural animation is described. Finally in Section 6, theresults and future directions are discussed.2 Related WorkA first approach for procedural animation is animationre-purposing. This technique uses an existing (prere-corded or handcrafted) animation and re-targets theanimation for a different purpose. For example, if acharacter climbs a ladder, the animation is re-purposedto suit different widths and step sizes of the ladder.Animation re-purposing can be implemented using in-verse kinematics [10] to adapt existing motion capturedata or handcrafted animations to new environments.A downside of this technique is that there is no methodto define the timings of the produced animations. An-other drawback is that one or more basic animationsstill have to be produced.The solution by Michiel van de Panne et. al [9] uti-lizes a real time physics simulation that creates an ani-mation by exerting forces on the limbs of the articulatedskeleton. Although this approach produces good visualresults for human characters (although with a slightlyrobotic feel), it is not feasible for an animation artistto adapt the synthesized motions for his own needs. Acommercial solution that takes this concept further isthe Endorphin commercial application.In the gaming industry blending [6] is commonlyused to create a wide range of animations from a smallerset of handcrafted animations. This way, for example,the animation artist provides animations for a charac-ter walking on surfaces with different inclinations (e.g.starting from -20 degrees to 20 degrees in incrementsof 4). The disadvantage here is that this increases theworkload on the animation artist, and even for a pro-duction with a large budget, this is a resource intensivesolution in terms of man-hours and introduces a largeamount of additional testing to verify that all the blendstate transitions do not produce artifacts.Motion graphs [7,8] are another method to produceanimations from motion capture data. Motiongraphs extract samples from motion capture data andmerge them together after the path planning stage toform the animations for use in a game. Although thisapproach is capable of capturing the style of the humanactor, the generated animations are over-fitted towardsthe animation from the motion capture data.Finally, Hidden markov models together with a ma-chine learning technique [12] have been used to learn thestyle (i.e. emotion) of (level) walking of a human char-acter. The generated animations are a blend of motioncaptured animations, but the generated animations cannot adapt to inclined surfaces.3 Animation System OverviewThe main difference with previous work is that this pa-per proposes a system that is parametrized in a waythat animation artists can understand, and that alsoallows the procedural system to learn from motion cap-ture data. The system is therefore not a black box sys-tem and gives the animation artist more fine grainedcontrol over the final output of the synthesized anima-tion. Furthermore, the system is based on control the-ory and this allows the synthesized animations to takethe environment into account.To synthesize animations an articulated skeleton(i.e. a rig) is used with eight degrees of freedom (hip,knee, ankle and ball of the foot of each leg) [3]. For afull simulation however eight degrees of freedom per legwould be required [11].The animation system receives the current rotationangles of the skeleton and the rotations towards the tar-get of the animation (in the case of walk animations,this is the target location of the foot), and outputs theangular velocities for the joints of the skeleton. The an-gular velocities are calculated with the help of a fuzzycontroller (FC). One advantage is that it is possibleto manually define smooth functions based on one ortwo input variables. A second advantage is that tech-niques [5] exist to convert a fuzzy controller to a fuzzyneural network (FNN). The parameters of the originat-ing fuzzy controller are visible as weights in the FNN,which makes it possible to adapt the parameters of theanimation after the network was trained with motioncapture data.For example, if a limb is required to rotate smoothlytowards a certain target position, a fuzzy logic variableReal-time Animation Of Human Characters With Fuzzy Controllers 3starttargetjointcenter startend α(◦)inputαif α is start then velocity is slowfast velocityslowstayoutputpositionif α is center then velocity is fastif α is center then velocity is stayFig. 1 Fuzzy logic control system for smooth movement ofa limb.α[◦]startrotationfastslowstaycenterendspeedFig. 2 Rotation speed in function of the fuzzy variable α.α (the angle between the current position of the limband the target) can be constructed with the member-ship functions start, center and end. It is physicallyimpossible to start moving at a large rotation speed.Therefore, if α belongs to the start set of the fuzzyvariable, the output fuzzy variable is set to slow. Like-wise, if α belongs to the center or end set, the outputfuzzy variable is set to fast and stay respectively. Thisfuzzy control system is shown in Figure 1.This simple set of rules and fuzzy variables, creates asmooth input/output relationship between the variableα and the output velocity. When the first derivativeof the output velocity is zero (or very small) at thestart and end positions, the generated animation has anease in / ease out [13] character. With simple rules andmembership functions it is possible to create complexdecision surfaces or functions, as shown in Figure 2.4 Controller DesignThere are many approaches to define the rules thatmake up the controller system. However with the statedgoal of an FNN that can learn from motion capture,there are a couple of considerations that need to beobserved. The first consideration is that all the param-eters for the animation must be defined in the fuzzycontroller, either explicitly by defining a fuzzy rule, orimplicitly by fine tuning the membership functions andcenterleftstartδend(a) Definition of δ as metricfor level walkingδlower leg(b) Definition of δ as metricfor ascentFig. 3 Angle δ as metric for joint rotationoutput angular velocities. This constraint ensures thatmotion capture data can be used to train the parame-ters of the controller. Furthermore, the number of mem-bership functions for each fuzzy variable has to be aslow as possible. Because we are creating controllers forthe sagittal plane only, this constraint is essential tocreate a system that is easily extensible. As a final con-sideration, the rules must be robust, in other words,the controllers must be able to cope or recover fromawkward start situations.With these considerations in mind, it was importantto define one or at most two metrics, for each joint in thearticulated skeleton. Some metrics ware easily found.For example, if the foot is placed on the ground, theankle joint will rotate based on the angle between thesole the foot and the surface. However, the ankle jointwill only rotate when the leg is swinging forward, thusa second metric is necessary to determine the timing ofthe rotation of the ankle. Experience learned that themetric that provides the best results was the angle ofthe upper leg with the hip-target segment (parameterδ in Figure 3a).This angle is not adequate however when the kneeis flexed at the end pose (for example in the case of as-cending a stair). In that case, the angle δ is determinedby calculating the end position of the knee (as definedin Figure 3b). Because the end position is dependent onthe location of the hip joint, the calculated intersectionis not a constant. However this does not pose a problemas the hip joint rotates monotonically towards its endlocation.In a similar manner as in Figure 1 rules can be de-fined that will guide the upper leg in swing phase to-wards the desired location, with a smooth input outputrelationship between the variable δ and the output ve-locity for the hip joint (Figure 4). Because the absolutevalue of δ can change however, and the membershipfunctions are constant, a scaling operation is performedon this parameter. For level walking, the parameter δis mapped to one if the upper leg has arrived at therequired location, and to minus one if the upper leg is4 Koen Samyn et al.centerstart end δinputif δ is start then hip velocity is extendslowextendextendstayoutputif δ is center then hip velocity is extendfastif δ is end then hip velocity is stayrotationspeedleftflexfastif δ is left then hip velocity is flexfast-1 0 1slow fastFig. 4 Upper leg control with ease-in ease-out behavior.jointangle[◦]cycle time [%]807060504030201000 10 20 30 40 50 60 70 80 90 100Stance phase Swing phasehipkneeFig. 5 Knee and hip joint reference rotation curves for levelwalking. [1, 2]-20-10010203040501 2time (s)hipkneeanklerotation(◦)(a) level walk, stepsize 60cm-20-10010203040501 2time (s)hipkneeanklerotation(◦)(b) level walk, stepsize 70cmFig. 6 Procedural joint angle curves for level walking forvarious step sizes.at its start position. The logical choice is then to mapδ to zero when the upper leg has a zero rotation anglerelative to the hip. Ascending a stair or steep terrainhas other dynamics then walking on flat terrain. It ishowever possible to use the same angle metric δ as de-fined in Figure 4. Except for the leg in stance phase,the rules are now very similar to the rules that weredefined for level walking.5 ResultsThe reference curves for level walking are given in Fig-ure 5. The rotational curves for the real time generatedlevel walking gait cycles are given in Figure 6 for twodifferent step sizes.Video footage illustrating the human gait on flatsurface and when walking a stair can be found on http://vimeo.com/user10476935/channels. The video frag-ment illustrates that it is possible to handtune fuzzycontrollers for level walking and ascending, for a hu-man (biped) character.6 Conclusion And Future WorkIn this paper it was shown that it is possible to createprocedural animations by manually tuning fuzzy con-trollers. However, to create convincing and realistic ani-mations it is necessary to tune the fuzzy controllers witha machine learning approach, and to engage animationartists that can help identify problem areas in the gener-ated animations. Another future machine learning ex-periment would try to discover relationships betweenthe input variables for the articulated skeleton. Finally,the system will extended to a full body simulation andmore basic animations (such as running, jumping andswimming).References1. Stair ascent and descent at different inclinations. Gait &Posture 15(1), 32 – 44 (2002)2. Roll-over characteristics of human walking on inclinedsurfaces. Human Movement Science 23(6), 807 – 821(2004)3. Aggarwal, J.K., Cai, Q.: Human motion analysis: a re-view. In: Nonrigid and Articulated Motion Workshop,pp. 90 – 102. IEEE (1997)4. Anonymous: Animation synthesis (2012). URL http://vimeo.com/user10476935/channels5. Berenji, H., Khedkar, P.: Learning and tuning fuzzy logiccontrollers through reinforcements. Neural Networks,IEEE Transactions on 3(5), 724 –740 (1992)6. Edsall, J.: Animation Blending: Achieving Inverse Kine-matics and More. Location Based Entertainment (2003)7. J.Chen, A.Steed: Planning plausible human animationwith environment-aware motion sampling. In: Motion inGames. ACM (2011)8. Kovar, L., Gleicher, M., Pighin, F.: Motion graphs. In:ACM SIGGRAPH 2008 classes, SIGGRAPH ’08, pp.51:1–51:10 (2008)9. van de Panne, M., Laszlo, J., Huang, P., Faloutsos, P.:Towards agile animated characters. Proceedings of the2000 IEEE International conference on Robotics & Au-tomation pp. 682–687 (2000)10. Rune, S.: Automated semi-procedural animation forcharacter locomotion. Master’s thesis, Aarhus University11. Sias F.R., J., Zheng, Y.: How many degrees-of-freedomdoes a biped need? In: Intelligent Robots and Systems’90. ’Towards a New Frontier of Applications’, Proceed-ings. IROS ’90. IEEE International Workshop on, pp. 297–302 vol.1 (1990)12. Tilmanne, J., Dutoit, T.: Continuous control of stylethrough linear interpolation in hidden markov modelbased stylistic walk synthesis. In: Cyberworlds (CW),2011 International Conference on, pp. 232 –236 (2011)13. Williams, R.: The animator’s survival kit. Faber, London,United Kingdom (2009)

Real-time animation of human characters with fuzzy controllers

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Real-time animation of human characters with fuzzy controllers

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