Abstract—Motion synthesis is a complex process, especially incases involving a variety of actions. In locomotion compositionprocesses, in which virtual characters are called upon to performa sequence of different actions, such as a sequence from walkingto running, the transition is mainly generated linearly. However,to accurately model the way in which humans actually performthe transition between two different actions, computer animationsmust also capture the nature of between-action processes. Thispaper presents a simple approach for animating the transitionprocess between different locomotion actions. The approachdetermines the relationship between the number of steps andthe velocity of the root. Results of the measurements are thenplotted on action transition graphs. Finally, the graphs are used inan experimental application that generates the correct transitionbetween the origin and target actions of the character, given boththe velocity of the root at the origin and at the target.
Realistic representations of a virtual character’s motion canbe easily achieved using technologies that capture high-qualitydata on the motions of actual humans performing differentactions [1]. Based on the recorded motion data, it is possible toaccurately synthesise motions involving various combinationsof recorded sequences. In addition, commercial applicationsgive the ability to assign these motion sequences to virtualcharacters, thus generating realistic animation sequences.
However, even when the provided motions are quite natu-ral, a variety of factors related to action transitions, which arethe focus of this study, have yet to be thoroughly examined.Hence, considering the different actions of a character, thesystem should be able to generate not only a smooth transitionbetween two different motion sequences, but also a human-likeaction transition that mimics the way actual humans performintermediary actions. Our assumption is that realistic repre-sentations of human motions require complex mathematicalmodels augmented by measurements that describe how actualhumans perform different actions.
The measurements required for generating transition se-quences are not always easy to obtain, as human bodies arecomplex articulated figures with many degrees of freedom(DOF); also, humans can perform different variations on thesame motion. Moreover, gender, age, and a variety of physicalproperties (e.g., height, weight) play an important role in
determining how each individual performs a given action.Thus, considering that generalisation of such transitions isdifficult, our study is restricted to transition processes involvingonly two different actions, such as the transitions betweenwalking, jogging, running, jumping, and stair stepping actions.
For this study, we obtained measurements on ten individ-uals, aged 18-27. The measurements included the number ofsteps and the velocity of the root during the transition fromone action to another. In using this approach to analyse motioncapture data, our goal was to enhance the naturalness of thegenerated motions of virtual characters who are called uponto perform different actions.
The remainder of this paper is organised as follows. Sec-tion 2 examines methods for measuring and understandinghow humans perform different actions. Section 3 outlines themethods used in the proposed solution, and Section 4 presentsan experimental application that generates transitions betweendifferent motions. Finally, Section 5 presents the conclusionsand discusses the possibilities for future work.
II. RELATED WORK
The use of motion capture data to produce new motionsequences has been thoroughly examined in recent years,and various methods have been proposed to generate thedesired motions of virtual characters. Each of the techniquesemployed to generate human-like locomotion sequences has itsown advantages and disadvantages. Multon et al. [2] analyseddifferent types of motion composition techniques for walkingmotions, and their survey provides the basic categories ofcomposition processes presented below.
The first category includes procedural techniques for gen-erating motions using algorithms based on biomechanics prin-ciples [3] [4] [5] [6]. While these motion models can provide ahigh level of control over the movements of a virtual character,the generated animations tend to be unnatural looking and onlyparticular behaviours may generated.
Another approach to synthesising and generating valid hu-man motions uses example-based techniques. In this approach,the synthesised motions are based either on motion concatena-tion, which combines motions for generating a new and longmotion sequence [7] [8] [9] [10], or motion parameterisationtechniques [11] [12] [13], in which reference motions areinterpolated so as to provide a new motion sequence fulfilling
CGAMES 2013 The 18th International Conference on Computer Games
user-defined parameters, such as the position of an end effector.Examples that combine these two techniques have also beenproposed [11].
In contrast, approaches based on measurements of humanperformance during different actions have not been thoroughlyexamined. Such measurements can give the ability to generatehuman-like motion sequences while analysing how humansperform different actions. Only a few such methods have beenproposed, such as the solution by Glardon et al. [14], inwhich an analysis of motion capture data generates the correctvelocity required of the character, as in the transition to jump-ing over an obstacle. However, Glardon et al.’s method hasonly been applied to actions involving walking, running, andjumping. Nevertheless, the results generated by this solutionare more human-like than those generated by approaches usingtechniques in which the analysis of locomotion is not takeninto account.
Other solutions that examine the ability to measure humanperformance and then pass the executed results to motion mod-els have come to the attention of crowd simulation researchers.For example, the solutions presented by Lee et al. [15] andLi et al. [16] use measurements of how humans performpath-planning processes to generate crowd flow models thatmimic human behaviour. The solution proposed by Lerneret al. [17] is based on evaluations of algorithmic represen-tations of crowd flow. The solution proposed by van Bastenet al. [18] uses motion capture data to understand collision-avoidance strategies involving human-human interactions. Therules obtained from these measurements are then applied toagent-based simulations to give more realistic simulations.
Although a general understanding of human motions hasbeen achieved, there is one basic limitation. The solutionproposed by Glardon et al. [14], which is similar to the solutionproposed in the present study, only examines the ability toadjust the motions of characters before a jumping tasks, ratherthan generating a general model that computes the optimalvelocity of the character at every step during the transitionprocesses. Thus, our solution employs the ability to measuretransitions based on the number of required steps. Hence, givena desired target motion for the character, the system is ableto execute the number of required steps, and to procedurallyincrease or decrease the velocity of the character depending onthe character’s origin and target velocity based on the transitiongraphs that are generated.
III. METHODOLOGY
This section presents the methodology used to achieve thedesired results. In the first step, each participant was calledupon to perform two different actions for each combinationof motions, as measuring only one action of the user wouldnot provide sufficient motion capture data. In addition, everyhuman performs the same action with different variations.Hence, each participant performed each action five times,which yielded data for 500 motion sequences. Figure 1illustrates the architecture of the approach.
A. Action Transition Components
Having retrieved the motion capture data, the next step inour method was to build the mapping between motions having
Fig. 1. Architecture of the proposed solution, which consists of off-line andon-line computations.
similar behaviours, such as between walking and running orbetween running and jumping. Then, for each motion, thevelocity of the root, which characterises the transition process,was assigned as the main parameter for understanding thedifferences between the individual actions employed in themotion sequence. More specifically, the velocity of the rootbetween two different actions should have a minimum or amaximum value that characterises one of the target actions.This approach was used to generate the desired velocity com-ponents assigned to each transition (see Table I for examples).For example, in the transition from a walking to runningmotion, the inverse transition, from running to walking, isexecuting by inversing the registered results, rather than usingmeasurements from the motion capture data. The inverseprocess is used because it is assumed that this procedure canprovide the desired result, as well as reduce the number ofgenerated transition graphs.
Based on Table I, it is possible to generate the velocity thatcharacterises each target action. However, as the velocity is notthe sole parameter, the number of steps taken to move fromthe origin to the target is also measured. This measurementis made using a simple foot detector [19], which recognisesfoot contact with the ground based on the velocity and heightof the foot. Using this approach, the velocity component thatcharacterises the target action is determined, as is the timeperiod for which the velocity of the action is maximised orminimised. It is then possible to determine the number of stepsrequired to generate the velocity representing the maximum orminimum value.
CGAMES 2013 The 18th International Conference on Computer Games
TABLE I. THE DISCRETE VELOCITY CHARACTERISTIC USEDBETWEEN TWO DIFFERENT ACTIONS FOR THE TRANSITION PROCESS.
VELOCITY COMPONENT WITH ASTERISK (*) DID NOT MEASURED.ALTHOUGH, THE INVERSE MEASUREMENTS ARE USED FOR DESCRIBING
EACH OF THOSE TRANSITIONS.
B. Action Transition Alignment
Having calculated the root velocity in accordance with thefoot contact approach, the next step of the method is to alignthe motions. The alignment process is based on the abilityto align the number of steps with the root velocity. Morespecifically, two features are extracted for each motion: thenumber of steps si and the velocity of the root vi at eachstep, such that each recorded motion mi is represented bymi = {(s1, v1), ..., (sn, vn)}, where n denotes the n − thregistered step of the human derived from the motion cap-ture data. Having extracted each action transition from thosecomponents, the next step is to align the motions (see Figure2) based on the i − thstep, where the i − th root velocityis equal to the maximum velocity vmax or the minimumvelocity vmin, depending on the discrete behaviour of theaction transition mentioned above. This alignment ensures thatall of the registered motions have the same transition behaviourand are aligned with the correct phase.
C. Transition Graphs
The proposed transition graphs represent the velocity of theroot of each motion, as based on the corresponding steps. Con-sidering that the number of steps for each registered motionvaries, depending on the transition process, it is necessary toidentify which steps should actually be included in the graphs.For the motion analysis process, it is necessary to removethe undesired steps (i.e., those that are not taking part of thetransition process). Thus, for computing the actual steps thatare required, the mean average of the number of steps beforeand after the velocity component is used to validate the numberof required steps for the transition process.
Having aligned the steps based on the velocity propertyand having removed the undesired steps, to generate correcttransition graphs, the actual numbers of steps and the rootvelocity are plotted, as illustrated in Figure 3. Based onthe graph, it is possible to develop a general approach tothe transition between two different actions by measuring themean root velocity at each step. Based on the mean averageof each action transition at each step, an action transitiongraph is constructed which presents the mean velocity of theroot at each step of the character, presented as maction =
Fig. 2. Alignment process of the step for which the velocity of the rootis assigned to the velocity component that characterises the target action. Anexample is given for before the alignment (upper plate) and after the alignment(lower plate). Each rectangle denotes a step; the yellow rectangles denote thestep at which the velocity component is executed.
{(s1, vmean1 ), ..., (sn, v
meann )}. With this general representa-
tion of the transition process, it is possible to understand howeach transition action should evolve. In addition, this meanaverage is used as the parameter for executing the transitionprocess during the runtime of the application, as is presentedin the following section.
IV. EXPERIMENTAL APPLICATION
Having presented the methodology for handling the motioncapture data and generating the action transition process,this section presents an experimental application designed togenerate the correct transition between two different motions.
A. Implementation
For the implementation of the proposed solution, all regis-tered motions are first downsampled to 60 fps. In addition, toenhance the collection of motion sequences, mirror versionsare designed for all motions. Finally, the action transitiongraphs are stored in a lookup table.
During the runtime of the application, the user is calledupon to choose the start and target actions of the character,as well as the velocity of the root. To avoid a deadlockin the system caused by choosing a velocity which is notcontained in the registered motion sequence, each differentaction is assigned a velocity with bounding limits, such asvaction ∈ [vmin, vmax]. Figure 4 shows the interface for theapplication. The system executes the most suitable motions soas to generate a desirable result.
B. Motion Generation
The system proposed in the previous section is able togenerate desirable transitions between two different actions.
CGAMES 2013 The 18th International Conference on Computer Games
Fig. 3. Velocity of the root versus the number of steps taken after the alignment process. At each step it is represented the minimum and maximum value ofthe velocity of the root.
Fig. 4. Interface of our application for generating a well transitioned motionsequence.
More specifically, the user is able to select the origin and thetarget motion, as well as the desired velocity of the roots forboth motions. The desired velocity of the origin motion is thevelocity of the character at step s1, and the velocity assigned tothe target action is the minimum or maximum velocity of theroot when the character reaches step sn. Based on user inputs,as well as on the corresponding transition graph, the systemfirst executes the required number of steps based on the targetvelocity. If the exact target velocity is not contained in thedatabase, the nearest neighbour velocity is used for executingthe number of steps.
In the second stage of the generation process, the systemshould be able to generate the desired motion by exactlyreaching the desired goal velocity of the character. However,a linear approximation of the velocity of the root at each step,achieved by connecting the velocity v1 of the first step with
the velocity vn of the last step, is not desirable. Hence, themean average graph that represents the general behaviour ofthe action transition process, scaled on the basis of user inputsfulfilling both the origin and the target velocity, is used as aparameter. Hence, a scalar parameter λ is computed from:
λ =‖v1 − vn‖
‖vmean1 − vmean
n ‖(1)
Then, the action transition process of the motion sequence iscomputed by multiplying the velocity of the root at each stepwith the parameter λ, resulting in a particular behaviour of theform mtransition = {(s1, λ ∗ vmean
1 ), ..., (sn, λ ∗ vmeann )}.
The final step of the action transition process is based onthe ability to execute and blend the desired motion sequencesat each single step of the character. Considering two motionsthat enclose the required velocity of the character such thatvchar ≤ vi and vchar ≥ vj for each step of the character, asderived from the mapping process mentioned above, the systemlinearly blends the velocity distances so as to provide thedesired motion, by assigning a weighted interpolation function.Hence, the desired velocity of the character is computed ateach k − th step, such that vs(k) = wivi + (1 − wi)vj ,where wi = vgraph/(‖vi − vj‖). Then, for each transitionstep, the nearest neighbour motions are extracted, followingthe previously mentioned blending process, to generate thefinal action transition. Finally, after having synthesised thecorresponding motions, a motion graph [7] synthesis processis employed to generate a smooth transition between theindividual steps of the character. Some examples of generatedmotions are illustrated in Figure 5.
V. CONCLUSION
This paper presents an approach for measuring action-transition processes between different locomotion behaviours.The proposed solution is based on the ability to analyse therecorded behaviours of the participants for each action transi-tion independently. Then, by analysing the motion capture data
CGAMES 2013 The 18th International Conference on Computer Games
Fig. 5. Example action transition motion generated using the proposedsolution. From jumping to stair stepping (upper row), from jumping to walking(middle row), and from running to walking (lower row).
based on the number of steps versus the velocity of the root, thedesired action-transition graphs are generated. These graphsprovide a more valid or natural-looking transition processduring the motion of the virtual character, thereby enhancingthe naturalness of the generated motion. Thus, in accordancewith our measurements, a simple experimental application thatis responsible for generating a locomotion sequence betweentwo different actions with given desired velocities at the startand target of the action is implemented.
One of the limitations of our current solution is relatedto the ability to synthesise action transitions based on variousother criteria, such as age, gender, and weight of the character;these limitations are imposed by the vast amount of experimen-tally captured data required to generate a desirable result usingmultiple variables. In the future, we would like to thoroughlyresearch transition processes involving multiple parameters,by measuring how actual humans with various characteristicsperform transition actions. In addition, we would like toresearch more deeply how this method can be implemented invarious automatically generated scenarios, where autonomouscharacters can perform various actions related to locomotionduring path planning. Finally, as the transition process hadnot previously been examined thoroughly, our future plansinclude the ability to measure the perceptions of humans whileaction transitions are being implemented, either in interactiveapplications such as in video games, or in static scenarios suchas in 3D videos. The results of such analyses should give us theability to understand how both users and participants/observersfeel about the ability to generate such transitions.
REFERENCES
[1] A. Menache, Understanding motion capture for computer animationand video games. Academic Press, 2000.
[2] F. Multon, L. France, M. Cani-Gascuel, and G. Debunne, “Computeranimation of human walking: a surve,” Journal of Visualization andComputer Animation, vol. 10, no. 1, pp. 39—54, 1999.
[3] R. Boulic, N. Thalmann, and D. Thalmann, “A global human walkingmodel with real-time kinematic personification,” The Visual ComputerJournal, vol. 6, no. 6, pp. 344–358, 1990.
[4] A. Bruderlin and T. Calvert, “Goal-directed, dynamic animation ofhuman walking,” in 16th Annual Conference on Computer Graphicsand Interactive Techniques. New York: ACM Press, 1989, pp. 233–242.
[5] P. Lv, M. Zhang, M. Xu, H. Li, P. Zhu, and Z. Pan, “Biomechanics-based reaching optimization,” The Visual Compute, vol. 27, no. 6-8, pp.613–621, 2011.
[6] W. Huang, M. Kapadia, and D. Terzopoulos, “Full-body hybrid motorcontrol for reaching,” in Motion in Games. Berlin Heidelberg: Springer-Verlag, 2010, pp. 36–47.
[7] L. Kovar, M. Gleicher, and F. Pighin, “Motion graphs,” ACM Transac-tions on Graphics, vol. 21, no. 3, pp. 473–482, 2002.
[8] O. Arikan, D. Forsyth, and J. O’Brien, “Motion synthesis from anno-tations,” ACM Transactions on Graphics, vol. 22, no. 3, pp. 402–408,2003.
[9] K. Pullen and C. Bregler, “Motion capture assisted animation: texturingand synthesis,” ACM Transactions on Graphics, vol. 21, no. 3, pp. 501–508, 2002.
[10] Y. Li, T.-S. Wang, and H.-Y. Shum, “Motion texture: a two-levelstatistical model for character motion synthesis,” ACM Transactions onGraphics, vol. 3, no. 2002, pp. 465—472, 21.
[11] R. Heck and M. Gleicher, “Parametric motion graph,” in Symposium onInteractive 3D Graphics and Games. New York: ACM Press, 2007,pp. 129–136.
[12] A. Yeuhi, C. Karen-Liu, and Z. Popovic, “Momentum-based parameter-ization of dynamic character motion,” Graphical Models, vol. 68, no. 2,pp. 194–211, 2006.
[13] Y. Huang and M. Kallmann, “Motion parameterization with inverseblending,” in Motion in Games. Berlin Heidelberg: Springer-Verlag,2010, pp. 242–253.
[14] B. R. T. D. Glardon, P., “On-line adapted transition between locomotionand jump,” in Computer Graphics International. IEEE, 2005, pp. 44–50.
[15] K. Lee, M. Choi, Q. Hong, and J. Lee, “Group behavior fromvideo: a data-driven approach to crowd simulation,” in ACM SIG-GRAPH/Eurographics symposium on Computer animation. Eurograph-ics Association, 2007, pp. 109–118.
[16] Y. L. anf M. Christie, O. Siret, R. Kulpa, and J. Pettre, “Cloning crowdmotions,” in ACM SIGGRAPH/Eurographics Symposium on ComputerAnimation. Eurographics Association, 2012, pp. 201–210.
[17] A. Lerner, Y. Chrysanthou, A. Shamir, and D. Cohen-Or, “Data drivenevaluation of crowds,” in Motion in Games. Berlin Heidelberg:Springer-Verlag, 2009, pp. 75–83.
[18] B. van Basten, J. Sander, and I. Karamouzas, “Exploiting motion captureto enhance avoidance behaviour in games,” in Motion in Games. BerlinHeidelberg: Springer-Verlag, 2009, pp. 29–40.
[19] B. van Basten, P. Peeters, and A. Egges, “The step space: examplebasedfootprintdriven motion synthesis,” Computer Animation and VirtualWorld, vol. 21, no. 3-4, pp. 433–441, 2010.
CGAMES 2013 The 18th International Conference on Computer Games
![Finger Motion Estimation and Synthesis for Gesturing ...web.ics.purdue.edu/~cmousas/papers/conf15-SCCGb.pdf[Mousas et al. 2014c], which are based on statistical analysis and synthesis](https://static.documents.pub/doc/80x56/5e59b9fbca1284273b7b1d30/finger-motion-estimation-and-synthesis-for-gesturing-webics-cmousaspapersconf15-sccgbpdf.jpg)
