Motion Level-of-Detail: A Simplification Method on Crowd Scene

Transcription

1 Moion Level-of-Deail: A Simplificaion Mehod on Crowd Scene Absrac Junghyun Ahn VR lab, EECS, KAIST ChocChoggi@vr.kais.ac.kr hp://vr.kais.ac.kr/~zhaoyue Recen echnological improvemen in characer animaion has increased he number of characers ha can appear in a virual scene. Besides, skeleal and mesh srucures are expeced o be more complex in he fuure. Therefore, simulaing massive characers oins in a real-ime crowd environmen wihou any preprocessing is unaffordable. We propose a preprocessing mehod called moion level-of-deail o overcome his limiaion. Our moion level-of-deail framework no only minimizes he simulaion cos of he oins, bu also mainains he similariy beween he original and he simplified moion. Join posure clusering (JPC), which is he skeleal simplificaion mehod of our framework, reduces skeleal node by he clusers of similar posures. A cluser is a se of coninuous frames, where each frame has similar posure. Because our approach depends on moion raecory, simplified resul preserves he qualiy of he moion. We also applied a geomeric simplificaion on deformable characer mesh, o increase performance. Our approach was paricularly useful for he complex skeleal moions ha have a monoonous raecory. Keywords: moion level-of-deail, clusering, crowd animaion, characer animaion, mesh simplificaion 1. Inroducion In he game and enerainmen indusry, a number of researchers are now rying o animae huge crowd scenes. As a resul, he simplificaion of a characer s moion is now a challenge for characer animaion. Non realime applicaion such as movie or animaion, compuing ime is no criical. However, a characer in he crowd scene does no ake much space on a rendered image. Therefore, Kwangyun Wohn VR lab, EECS, KAIST wohn@vr.kais.ac.kr hp://vr.kais.ac.kr simulaing deail moion on each characer is a wase of compuing resource. Besides, in a real-ime applicaion such as game or NVR environmen, adusing compuaion vs. animaion qualiy is as much imporan as well known compuaion vs. image qualiy. In his paper, we presen he moion levelof-deail framework which is an effecive preprocessing mehod in he real-ime crowd environmen. We are subeced o moion capured daa, because game and movie indusry also prefer o uilize moion capure raher han physical simulaion. The moion level-of-deail framework was designed similarly o he common real-ime virual environmen. As shown in figure 1, seleced moion from he moion daabase is mapped o he seleced characer from he characer daabase. Afer he mapping process, characer is ransformed o he virual world. However, a lo of oin ransformaion is required on each process and generaing moion wihou preprocessing is inefficien. Therefore, o enhance he performance of our framework, we resolved he problem from he perspecive of simulaion (skeleal simplificaion) and rendering (geomeric simplificaion). Figure 1 : Moion level-of-deail framework Skeleal simplificaion: Every moion includes he hierarchy of ariculaed figure. This skeleal srucure can be defined as a ree, whose node s (oin) aribues (posiion, roaion)

2 change every frame. By simplifying he skeleon, we can reduce he cos of ransforming oins, which is serious on crowd scenes. Moreover, in he skinning process we can reduce he overheads for verex ransformaion. Previous work has inroduced a simplificaion mehod of ariculaed figure [1][2][20]. Bu hese works need manual preprocess and canno be applied o crowd moion. We describe hese works in deail in he nex secion. Geomeric simplificaion: Prior researchers on geomeric simplificaion focused on a rigidbody mesh srucure [3][4][5] o reduce rendering cos. However, characer mesh dynamically deforms every frame. Because of ha, characer mesh mus be localized by each body pars. To improve he rendering performance, previous research has subsiued characer geomery for an image [6][7][21]. We expec a remendous improvemen on performance, bu he moion is no realisic and has many limiaions on he camera work. Our moion level-of-deail framework applied hese skeleal (JPC) and geomeric simplificaion. Moion dependen mehod is required o auomae skeleal simplificaion in any kind of moions. Therefore, we firs analyzed he raecories of each oin. By ploing each raecory on he uni sphere, we can separae he clusers of posures. Correc clusers of posures are evaluaed by an error meric beween wo posures. The error values are applied o our clusering able. Opimal posure clusers are classified by he error hreshold and we applied he level-of-deail a he run-ime. A oin is dynamically simplified using clusered informaion. Every clusered area inside a oin raecory keeps he key posure. A he run-ime, our sysem ransforms he key posure and curren oin s informaion is updaed o his paren, unil curren clusered area of oin is finished. JPC mehod auomaically generaes muli-resoluion moion and increase performance wihou loss of animaion qualiy. However, variable moion, which posures of oins change quickly are no much effecive, because of hierarchy reconsrucion on every cluser. We also implemened a simple mesh simplificaion mehod o enhance performance. Our mehod is useful in a real-ime ineracive crowd environmen. Afer a reviewing relaed works in secion 2, we analyze boleneck of moion generaion in secion 3. In secion 4, we describe he oin posure clusering (JPC) mehod. In secion 5, we show how we applied geomeric simplificaion o a characer mesh srucure. Finally, in secion 6, we show he resuls of our implemenaion and experimens. 2. Relaed works In his secion we describe he research on crowd animaion. Crowd animaion falls ino hree maor subecs: Firs for an ideal sysem of crowd animaion, he realiy of he generaed scene mus be saisfied; second, ineraciviy is imporan because he user or animaor of a crowd sysem mus be able o access he individual characers of a scene; and finally, enhancing he overall performance of he sysem is imporan for a massive crowd scene. Scene realiy: To represen a realisic crowd scene in he virual world, crowd modeling is required o conrol an individual characer s moion [8][9]. Analyzing a human-like crowd flow is also imporan. Some researchers analyzed he crowd flow in an emergency siuaion [10][11]. Anoher maor facor for mainaining realiy is he se of behavioral rules for each individual. Behavioral rules are well defined for he fishes in an aquarium [12]. The behavior of he fish is simulaed by he curren sae of he virual environmen. The behavior of human crowd has been similarly defined [13][14]. Collision deecion of individuals in a crowd [15] and moion ransiion [16] are also imporan behavioral facors of he scene realism. Ineraciviy: The behavioral rules defined for each individual are inadequae for an animaor who designs crowd scene. The animaor someimes needs ineracion o convey he inended behavior of each characer. For his ype of siuaion, some of he early research on he ineracion and scriping of crowds is a good reference [17] [18]. Performance: Simulaing complex and massive crowds every frame is ime consuming. The problem is more serious when simulaing many characers a he same ime. The research on improving performance of a crowd scene covers wo maor opics. The firs maor opic is he simplificaion of he skeleal srucure for a rapid physical simulaion. A manually simplified hierarchy

3 for moion blending has presened o enhance performance of physical simulaion and faciliae convergence of he opimizaion [1]. Three basic principles DOF removal, node subree removal and symmeric movemen removal are inroduced in his work. Anoher work has inroduced full physics kinemaics cener of mass model of a simple ariculaed obec o simplify physical simulaion [2]. However, hese skeleal simplificaions are unable o apply in he crowd moion using moion capure daa. We are also able o reference recen work ha has focused on realime nework environmen [20]. The oher maor opic is he applicaion of an image-based echnique o each characer [6][7][21]. They sampled image sequence of characer s moion from each direcion and used an imposer or billboard echnique o enhance performance. Alhough we can esimae a good performance using imagebased echniques, he resul of he sampled moion is no realisic and he camera work has many limiaions. To overcome hese limiaions, we propose a new simplificaion framework in a real-ime crowd environmen. 3. Moion boleneck Rendering boleneck by processing a highly deailed geomeric model is well known in he compuer graphics field. However, a boleneck ha occurs by oin processing is no a common knowledge. In his secion, we define he process of moion generaion and analyze he performance by simulaing oins. 3.1 Moion process The process of generaing a moion o a characer is shown in figure 2. During he preprocessing, curren animaion sysem aligns he seleced moion / characer srucure and calculaes difference marix beween hem. A he run-ime, he sysem ransforms he oin posure from he moion daabase and applies he difference marix. In he skinning process, he verices and normals of he characer mesh are deformed by he curren generaed oin posure. Finally, he sysem ransforms enire posure resul o he virual scene. This sequence can be defined as he scene generaion of characer animaion. A moion boleneck occurs in ha process. Figure 2: Moion process If he processing ime for muliplying he verex on he ransformaion marix is p, he cos of muliplying he marix is 4p. The oal complexiy of a oin can be defined as (8L+2N)p, where L is he number of oins from he curren node o he leaf node, and N is he number of mesh verices conneced o he curren body segmen. To analyze he moion boleneck in a crowd scene, we conduced an experimen in which we animaed 1000 characers. Each characer consiss of 946 polygons and 50 rigid oins. The resul of he experimen in figure 3 shows ha he moion boleneck is challenging, especially for ineracive crowd environmen. Figure 3: Compuing ime raio on crowd scene 3.2 Defining moion We defined moion as a ree srucure in which he head, hands and fee are leaf nodes and he pelvis is he roo. Moion is herefore assumed o be a general ree definiion such as T(N, E). The oin values such as posiion and orienaion are assumed o be node aribues and he oin link forms a rigid segmen. We define moion M on ime as follows: M ( ) = M ( J ( ), S( )) J ) = { ( ), ( ), ( ),..., ( ),..., ( )} ( n { p ( ), q ( )} if = roo oin ( ) = { NIL, NIL} if k = leaf oin { NIL, q ( )} oherwise S ) = { s ( ), s ( ),..., s ( ),..., s ( )} ( 1 2 n s ( ) = { ( ), p ( ), l, p ( Where J() is he se of oin nodes, S() is he se of segmen edges a ime, and () is he -h oin a ime. Only he roo oin has posiion values, because oin represenaion is based on a local coordinae. Because he leaf )}

4 nodes are end-effecors, he posiion and orienaion values on he leaf are unavailable. Every oin has a paren, excep for he roo. Therefore, =0 is excluded from S(). The parameer s () is he link beween he -h oin and he -h paren wih is lengh l,p (). 4. Join posure clusering (JPC) The roaional effec of a local oin is updaed every frame even hough he difference is insignifican. By ploing he oin raecory on a uni sphere, we found some groups of frames ha needed no ransformaion. We grouped similar posures by analyzing he enire moion raecory. By his mehod, n frames of moion were reduced o m groups of posure (m<n). To minimize he error cos of he moion difference beween he original moion M() and he simplified moion M (), he original moion became a superse of he simplified moion (M() M ()). The simplified oin mainains he key posure of he curren cluser and sends is own informaion o he paren oin. 4.1 Error evaluaion To generae opimal clusers, we defined he error cos beween posures. The difference beween wo posures a oin is defined as equaion (1): of he local oins a ime is expressed as equaion (2): v ', c 1 ( ) = ( q ( ) v q ( ) ) / l ( ) (2) The raecory informaion v () is he vecor posiion ploed on he uni sphere. The scalar value l,c () is he segmen lengh beween oin and is child oin c. Angle difference: Using he equaion (2), we can simply calculae he difference angle beween and ref as follows: v '( ) v '( ref ) θ ref ) = arccos( ) (3) 2 l ( ), c Weighing facor: A oin wih many children, which has a higher weighing value han a oin near a leaf, propagaes a large error cos. The parameer r () is he weighing facor ha is applied o he posiion difference. I is defined as follows: leaf r ( ) = l c ( ) (4), c E ref ) = E pos, ref ) + αeori, ref ) (1) The error is he sum of he posiion and he orienaion error. The ime is he curren frame and ref is he esimaed key frame. The consan α is he weighing value of he orienaion difference, and he weighing value is se o zero when oin has no wis roaion. By using his measuremen we clusered each group of posures. The posiion error was generaed by he local oin raecory and he angle difference beween he wo posures. The orienaion error is defined as he logarihm of he quaernion. Posiion and orienaion error are formulaed by measuremen values as shown in figure 4. Local oin raecory: If he base pose vecor from oin o is child is v, and he orienaion of he oin a ime is q (), he raecory v () Figure 4: Measuremen values Posiion error: The posiional error cos beween he original and he clusered posures a oin, which means he posiion difference by simplifying posure o ref, is defined as equaion (5): E ) = r ( ) θ ) (5) pos, ref ref

5 Orienaion error: Minimizing an error wih only he raecory of oin can cause a loss of he wised roaion on each oin. We applied a roaional measuremen of oin using he logarihm of quaernion [16][19], which means he orienaion difference by simplifying posure o ref. The equaion is expressed as follows: E Figure 5: Opimal cluser generaion by using clusering able (oin difference map) 1 ) = 2 log( q ( ) q ( )) (6) ori, ref ref Figure 5 shows he process of generaing opimal cluser from he oin difference map. Every diagonal value is 0 and he map is almos symmeric. Difference beween symmeric pairs are occurred by he weighing facor r (). We used only he upper riangle, since he able is almos symmeric. The lower riangle was used only for he process of rerieving he key posure. The opimum se of he clusers are derived by he following algorihm: 4.2 Clusering able To generae he opimum cluser ha does no exceed he error hreshold, we used n x n difference map (clusering able) of oin posures, where n is he oal number of frames. The able forms a marix of he error cos beween wo posures in a moion sequence. The elemens of he able are E (, ref ), and if = ref, he value of he elemen is zero, because he same posures have no difference. A cluser is seleced when he op-lef coordinae ( ref, ) has he same value and he size of he mask is m x m (m n). A leas one row inside he mask mus be lower han he error hreshold. STEP 1: Generae available range on each row LOOP START STEP 2: Search MAX range STEP 3: Se range o final cluser lis STEP 4: Delee curren MAX range STEP 5: Refresh remaining range LOOP END STEP 6: Rerieve key posure on each cluser In sep 1, he range of each row is generaed from he diagonal elemen (upper riangle elemen) unil he elemen of he able is higher han he error hreshold. Every oplef coordinae of he mask mus saisfy ( ref, ).

6 In sep 5, we mus refresh he remaining range because an inerseced area exiss beween removed range and remaining range. If he size of he refreshed range is 1, he range is also removed. The cluser lengh mus be greaer han Key posure Afer he final cluser lis was generaed, we exraced he key posure of he clusered area. The enire cluser mainains he key posure in he moion sequence. The key posure is he minimum sum of he elemens in he same row. The key posure is generaed as equaion (7), where m is he range of he opimal cluser. m ( E ref ref )) min (7) As he resul, he minimum reference frame 165 means ha every frame from 160 unil 166 is mainained o posure 165. Mainaining posure 165 is he minimum cos of error compared wih oher reference frames. Figure 6 shows he raecory of a oin afer he JPC process. As he error hreshold increases, he number of clusers decreases. 5. Geomeric Simplificaion Moion reconsruced by JPC mehod should be more improved by combining a common geomeric simplificaion. In characer animaion, however, he mesh deforms dynamically on each frame. Every verices and weighing values are linked o he oin segmen and hiry percen of full-body mesh verices are shared by more han wo oin segmens. Shared verices are deformed from he local coordinae by weighing values. We divided he verex lis by each oin and applied progressive mesh [3] o each oin segmen. Before applying he mesh simplificaion, we divided he local verex lis ino he shared verex area and he non-shared verex area. The weighing value of he shared verex is (0<w<1), and of he non-shared verex, (w=1). Figure 7: Mesh simplificaion resul Figure 6: Clusered raecory If he moion daabase consiss of long size moions, we need o reduce he problem, because he problem complexiy is O(n 2 ). Before consrucing he able, we applied he angle velociy of he moion o separae he range of he able. As we described in equaion (6), he posiion error is in r, Θ form. Therefore he reducion measuremen can be defined as E pos, (, -1). 6. Experimens and Resuls 6.1 Experimen resul Skeleal simplificaion: JPC mehod exracs maximum size of he cluser inside error bound. Therefore, he original frame posure and he key posure of he cluser are differen. As we shown in figure 8, 9, 10, low error moion is more similar o he original moion. We did experimens abou moion wih small number of oins (figure 8) vs. large number of oins (figure 9) and variable moion (figure 9) vs. monoonous moion (figure 10). Table 1 shows average simplified oins rae (%) per frame. The simplificaion rae of a moion wih large number of oins is higher han a moion wih small number of oins, because oin densiy of he former is higher han he laer moion. The oin raecory of a monoonous moion can exrac larger size of cluser han a variable moion, because he

7 monoonous moion has smaller raecory area han he variable moion. is more complex and he number of characers in he crowd scene increase, he processing ime of he simplificaion mus be enhanced. Figure 8: Dance (28 oins, variable moion) Figure 11: Crowd scene wih 256 characers Figure 9: Push (80 oins, variable moion) Figure 10: Look (80 oins, monoonous moion) Moion Original E=5.0 E=50.0 E=100.0 E=300.0 Dance Push Look Table 1: Average simplified oins (%) Overall simulaion: We implemened a realime crowd environmen wih moion level-ofdeail framework o compare he processing ime of each experimen resul. As we shown in figure 11, we loaded 256 characers wih 28 oins dance moion. A characer model has 471 verices and we applied geomeric simplificaion on each characer. We also applied skeleal simplificaion using JPC mehod. The experimen resul is depiced in figure 12. The processing ime of a frame using only geomeric simplificaion is a lile bi beer han he skeleal simplificaion. However, by applying boh mesh and skeleal simplificaion, we obained a beer resul han any oher experimens. If he ariculaed figure Figure 12: Experimen resul of he crowd scene 6.2 Conclusion and fuure work In his paper, we presened he moion levelof-deail framework o enhance processing ime of he real-ime crowd environmen. To mainain he visual qualiy, we applied a moion dependen skeleal simplificaion mehod. Moreover, geomeric simplificaion is considered o our framework o improve our sysem performance. We conrolled he deail of he moion and he geomery by he disance beween he camera and he characer s posiion. As he resul of our experimens, he image and he animaion qualiy was affordable. Our preprocessing mehod was paricularly useful o he monoonous moion and complex ariculaed figure s moion wih many oins. Because of he ree reconsrucion ime, variable moion was less efficien han he monoonous moion. Using our mehod o he complex crowd scene like urban or game environmen, we expec a more enhanced performance, since in ha kind of environmen a lo of characers are occluded during he moion sequence. We esimae ha he number of characers and he

8 complexiy of oin and mesh srucure in he crowd scene will increase in he near fuure. The simplificaion mehod in he crowd scene will surely be a more challenging issue. Fuure improvemen is needed o our framework. Firs, we didn consider abou view-dependen simplificaion. By applying view-dependen mehod, we should be able o recover more enhanced resul. In his paper, we applied radiional geomeric simplificaion, and we simplified moion and mesh independenly. Anoher work we need o improve is embedding he local geomeric par o he oin segmen. By embedding he geomery o he oin segmen, we should be able o conrol he moion and he mesh deail simulaneously and expec a more affordable simplificaion resul. Acknowledgemens This research has been funded by Seoul Insiues of he Ars. We would like o hank KAIST VR Lab, Kaydara Moion Builder and HiWin for providing characer moions and models. References [1] Z. Popovic and A. Wikin. Physically based moion ransformaion. ACM SIGGRAPH 99, pages 11-20, [2] D. Carlson and J. Hodgins. Simulaion levels of deail for real-ime animaion. Graphics Inerface 97, [3] H. Hoppe. Progressive mesh. ACM SIGGRAPH 96, pages , [4] M. Garland and P. Heckber. Mesh simplificaion wih quadric error merics. ACM SIGGRAPH 97, pages , [5] H. Kim and K. Wohn. Muliresoluion model generaion wih geomery and exure. VSMM 2001, [6] F. Tecchia and Y. Chrysanhou. Realime rendering of densely populaed urban environmens. 10h Eurographics Workshop on Rendering, pages 45 56, [7] A. Aubel, R. Boulic and D. Thalmann. Real-ime display of virual humans: Level of deails and imposors. IEEE Transacions on Circuis and Sysems for Video Technology, Special Issue on 3D Video Technology, 10(2): , [8] E. Bouvier, E. Cohen and L. Naman. From crowd simulaion o airbag deploymen: paricle sysems, a new paradigm of simulaion. Journal of Elecronic Imaging, 6(1): , [9] S. Musse and D. Thalmann. Hierarchical model for real ime simulaion of virual human crowds. IEEE Transacions on Visualizaion and Compuer Graphics, 7(2): , [10] D. Helbing. A fluid-dynamic model for he movemen of pedesrians. Complex Sysems, pages , [11] S. Shekhar and Q. Lu. Evacuaion planning algorihms: A capaciy consrained rouing approach [12] X. Tu and D. Terzopoulos. Arificial fishes: physics, locomoion, percepion, behavior. ACM SIGGRAPH 94, [13] D. Brogan and J. Hodgins. Group behaviors for sysems wih significan dynamics. Auonomous Robos, 4: , [14] S. Musse and D. Thalmann. A model of human crowd behavior. Workshop of Compuer Animaion and Simulaion of Eurographics 97, [15] C. Reynolds. Flocks, herds and schools: A disribued behavioral model. ACM SIGGRAPH 87, [16] T. Kim, S. Park and S. Shin. Ryhmic moion synhesis based on moion bea analysis. ACM SIGGRAPH 03, [17] B. Blumberg and T. Galyean. Muli-level direcion of auonomous creaures for real-ime virual environmens. ACM SIGGRAPH 95, pages 47 54, [18] K. Perlin and A. Goldberg. Improv: A sysem for scriping ineracive acors in virual worlds. ACM SIGGRAPH 96, pages , [19] J. Lee, J. Chai, P. Reisma, J. Hodgins and N. Pollard. Ineracive conrol of avaars animaed wih human moion daa. ACM SIGGRAPH 02, [20] T. Di Giacomo, C.Joslin, S. Garchery, N. Magnena-Thalmann. Adapaion of Facial and Body Animaion for MPEGbased Archiecures. IEEE Cyberworlds, pages 221, [21] F. Tecchia, C. Loscos and Y. Chrysanhou. Image Based Crowd Rendering. IEEE CG&A March/April, pages 36-43, 2002.