MARC 主機 00000nam  2200829 i 4500 
001    7347033 
003    IEEE 
005    20151125102138.0 
006    m    eo  d         
007    cr cn |||m|||a 
008    151124s2016    caua   foab   000 0 eng d 
020    9781627058292|qebook 
020    |z9781627058285|qprint 
024 7  10.2200/S00673ED1V01Y201509CGR020|2doi 
035    (CaBNVSL)swl00405826 
035    (OCoLC)930370838 
040    CaBNVSL|beng|erda|cCaBNVSL|dCaBNVSL|dAS|dIIS 
050  4 QA76.9.C65|bK268 2016 
082 04 003.3|223 
100 1  Kapadia, Mubbasir.,|eauthor 
245 10 Virtual crowds :|bsteps toward behavioral realism /
       |cMubbasir Kapadia, Nuria Pelechano, Jan Allbeck, Norm 
       Badler 
264  1 San Rafael, California (1537 Fourth Street, San Rafael, CA
       94901 USA) :|bMorgan & Claypool,|c2016 
300    1 online resource (xxi, 248 pages) :|billustrations 
336    text|2rdacontent 
337    electronic|2isbdmedia 
338    online resource|2rdacarrier 
490 1  Synthesis lectures on visual computing,|x2469-4223 ;|v# 20
500    Part of: Synthesis digital library of engineering and 
       computer science 
504    Includes bibliographical references (pages 219-245) 
505 0  1. Introduction -- 
505 8  Part I. Multi-agent collision avoidance -- 2. Background -
       - 2.1 Centralized approaches -- 2.2 Agent-based approaches
       -- 2.2.1 Data-driven approaches -- 2.2.2 Predictive 
       approaches -- 2.3 Locomotion synthesis -- 2.4 Challenges 
       and proposed solutions -- 2.4.1 Particle-based agent 
       models -- 2.4.2 Decoupling between steering and locomotion
       -- 2.4.3 Generalization and applicability of data-driven 
       approaches -- 3. Footstep-based navigation and animation 
       for crowds -- 3.1 Introduction -- 3.2 Locomotion model -- 
       3.2.1 Inverted pendulum model -- 3.2.2 Footstep actions --
       3.2.3 Locomotion constraints -- 3.2.4 Cost function -- 3.3
       Planning algorithm -- 3.4 Evaluation -- 3.4.1 Interfacing 
       with motion synthesis -- 4. Following footstep 
       trajectories in real time -- 4.1 Animating from footsteps 
       -- 4.2 Framework overview -- 4.3 Footstep-based locomotion
       -- 4.3.1 Motion clip analysis -- 4.3.2 Footstep and root 
       trajectories -- 4.3.3 Online selection -- 4.3.4 
       Interpolation -- 4.3.5 Inverse kinematics -- 4.4 
       Incorporating root movement fidelity -- 4.5 Results -- 
       4.5.1 Foot placement accuracy -- 4.5.2 Performance -- 5. 
       Context-sensitive data-driven crowd simulation -- 5.1 
       Steering in context -- 5.2 Steering contexts -- 5.3 
       Initial implementation -- 5.3.1 Training data generation -
       - 5.3.2 Oracle algorithm -- 5.3.3 Decision trees -- 5.3.4 
       Steering at runtime -- 5.4 Results -- 5.4.1 Classifier 
       accuracy -- 5.4.2 Runtime -- 5.4.3 Collisions -- 6. 
       Conclusion -- 6.1 Footstep-based collision avoidance -- 
       6.2 Footstep-based locomotion -- 6.3 Context-based 
       steering -- 
505 8  Part II. Multi-agent navigation -- 7. Background -- 7.1 
       Navigation meshes -- 7.2 Planning -- 8. Navigation meshes 
       -- 8.1 NavMeshes from 3D geometry: NEOGEN -- 8.1.1 GPU 
       coarse voxelization -- 8.1.2 Layer extraction and labeling
       -- 8.1.3 Layer refinement -- 8.1.4 NavMesh generation -- 
       8.2 Results -- 8.3 Limitations and discussion -- 9. Multi-
       domain planning in dynamic environments -- 9.1 Multi-
       domain planning -- 9.2 Overview -- 9.3 Planning domains --
       9.3.1 Multiple domains of control -- 9.4 Problem 
       decomposition and multi-domain planning -- 9.4.1 Planning 
       tasks and events -- 9.5 Relationship between domains -- 
       9.5.1 Domain mapping -- 9.5.2 Mapping successive waypoints
       to independent planning tasks -- 9.6 Results -- 9.6.1 
       Comparative evaluation of domain relationships -- 9.6.2 
       Performance -- 9.6.3 Scenarios -- 10. Conclusion -- 
505 8  Part III. Perception -- 11. Background -- 12. Sound 
       propagation and perception for autonomous agents -- 12.1 
       Sound categorization and representation -- 12.1.1 Sound 
       feature selection and categorization -- 12.1.2 Sound 
       packet representation (SPR) -- 12.1.3 SPR selection for 
       hierarchical cluster analysis -- 12.2 Sound packet 
       propagation -- 12.2.1 Transmission line matrix using 
       uniform grids -- 12.2.2 Pre-computation for TLM using a 
       quad-tree -- 12.3 Sound perception and behaviors -- 12.3.1
       Effect of sound degradation on perception -- 12.3.2 
       Hierarchical sound perception model -- 12.3.3 Sound 
       attention and behavior model -- 12.4 Experiment results --
       12.4.1 Applications -- 13. Multi-sense attention for 
       autonomous agents -- 13.1 Introduction -- 13.2 Methodology
       -- 13.2.1 Object and action representations -- 13.2.2 
       Sense preprocessing -- 13.2.3 Sensing -- 13.3 Hierarchical
       aggregate clustering -- 13.3.1 Environment-centric 
       clustering -- 13.3.2 Agent-centric clustering -- 13.3.3 
       Aggregate properties -- 13.4 Analysis and results -- 14. 
       Semantics in virtual environments -- 14.1 Incorporating 
       semantics -- 14.1.1 Lexical databases -- 14.1.2 
       Modularized smart objects -- 14.2 Semantic generation -- 
       14.2.1 Hierarchy generation -- 14.2.2 Semantic 
       modularization -- 14.2.3 Runtime performance -- 14.3 
       Limitations -- 15. Conclusion -- 
505 8  Part IV. Agent-object interactions and crowd heterogeneity
       -- 16. Background -- 17. Parameterized memory models -- 
       17.1 Memory system -- 17.1.1 Memory representation -- 
       17.1.2 Sensory memory -- 17.1.3 Working memory -- 17.1.4 
       Long-term memory -- 17.2 Example and analysis -- 17.3 
       Future work -- 18. Individual differences -- 18.1 
       Personality -- 18.1.1 Personality-to-behavior mapping -- 
       18.1.2 User studies on personality -- 18.2 Roles and needs
       -- 18.2.1 Approach -- 18.2.2 Implementation -- 19. 
       Conclusion -- 
505 8  Part V. Behavior and narrative -- 20. Background -- 21. An
       open source platform for authoring functional crowds -- 
       21.1 ADAPT -- 21.2 Framework -- 21.2.1 Full-body character
       control -- 21.2.2 Steering and path-finding -- 21.2.3 
       Behavior -- 21.3 Shadows in full-body character animation 
       -- 21.3.1 Choreographers -- 21.3.2 The coordinator -- 
       21.3.3 Using choreographers and the coordinator -- 21.3.4 
       Example choreographers -- 21.4 Character behavior -- 
       21.4.1 The ADAPT character stack -- 21.4.2 Body 
       capabilities -- 21.5 Character interactions -- 21.5.1 
       Characters interacting with each other -- 21.5.2 
       Characters interacting with the environment -- 21.6 
       Results -- 21.6.1 Multi-actor simulations -- 21.6.2 
       Computational performance -- 22. Event-centric planning 
       for narrative synthesis -- 22.1 Problem domain and 
       formulation -- 22.1.1 State space -- 22.1.2 Action space -
       - 22.1.3 Goal specification -- 22.2 Planning in event 
       space -- 22.3 Runtime and simulation -- 22.3.1 Event 
       loading and dispatch -- 22.3.2 Handling dynamic world 
       changes -- 22.3.3 Intelligent ambient character behavior -
       - 22.4 Results -- 22.4.1 Environment design -- 22.4.2 
       Object state description -- 22.4.3 Authored events -- 
       22.4.4 Generated narrative -- 22.4.5 Reacting to user 
       intervention -- 23. Conclusion -- 24. Epilogue -- 
505 8  Bibliography -- Authors' biographies 
506    Abstract freely available; full-text restricted to 
       subscribers or individual document purchasers 
510 0  Compendex 
510 0  INSPEC 
510 0  Google scholar 
510 0  Google book search 
520 3  This volume presents novel computational models for 
       representing digital humans and their interactions with 
       other virtual characters and meaningful environments. In 
       this context, we describe efficient algorithms to animate,
       control, and author human-like agents having their own set
       of unique capabilities, personalities, and desires. We 
       begin with the lowest level of footstep determination to 
       steer agents in collision-free paths. Steering choices are
       controlled by navigation in complex environments, 
       including multi-domain planning with dynamically changing 
       situations. Virtual agents are given perceptual 
       capabilities analogous to those of real people, including 
       sound perception, multi-sense attention, and understanding
       of environment semantics which affect their behavior 
       choices. The roles and impacts of individual attributes, 
       such as memory and personality are explored. The animation
       challenges of integrating a number of simultaneous 
       behavior and movement demands on an agent are addressed 
       through an open source software system. Finally, the 
       creation of stories and narratives with groups of agents 
       subject to planning and environmental constraints 
       culminates the presentation 
530    Also available in print 
538    Mode of access: World Wide Web 
538    System requirements: Adobe Acrobat Reader 
588    Title from PDF title page (viewed on November 24, 2015) 
650  0 Crowds|xComputer simulation 
650  0 Collective behavior|xComputer simulation 
650  0 Intelligent agents (Computer software) 
653    computer graphics 
653    crowd simulation 
653    computer animation 
653    agent simulation 
653    steering 
653    navigation 
653    semantic modeling 
653    agent perception 
653    sound 
653    attention 
653    behavior selection 
653    narrative 
653    digital storytelling 
653    pathfinding 
653    behavior authoring 
700 1  Pelechano, Nuria.,|eauthor 
700 1  Allbeck, Jan M.,|eauthor 
700 1  Badler, Norman I.,|eauthor 
776 08 |iPrint version:|z9781627058285 
830  0 Synthesis digital library of engineering and computer 
       science 
830  0 Synthesis lectures on visual computing ;|v# 20.|x2469-4223
856 41 |uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=7347033
       |zeBook(IEEE-MORGAN)