Muutke küpsiste eelistusi

Virtual Crowds: Steps Toward Behavioral Realism [Pehme köide]

  • Formaat: Paperback / softback, 270 pages, kõrgus x laius: 235x191 mm, kaal: 333 g
  • Sari: Synthesis Lectures on Visual Computing
  • Ilmumisaeg: 01-Nov-2015
  • Kirjastus: Morgan and Claypool Life Sciences
  • ISBN-10: 1627058281
  • ISBN-13: 9781627058285
Teised raamatud teemal:
  • Formaat: Paperback / softback, 270 pages, kõrgus x laius: 235x191 mm, kaal: 333 g
  • Sari: Synthesis Lectures on Visual Computing
  • Ilmumisaeg: 01-Nov-2015
  • Kirjastus: Morgan and Claypool Life Sciences
  • ISBN-10: 1627058281
  • ISBN-13: 9781627058285
Teised raamatud teemal:
Presents novel computational models for representing digital humans and their interactions with other virtual characters and meaningful environments. In this context, the authors describe efficient algorithms to animate, control, and author human-like agents having their own set of unique capabilities, personalities, and desires.

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.
Preface xix
Acknowledgments xxi
1 Introduction
1(4)
Part I Multi-Agent Collision Avoidance 5(48)
2 Background
7(6)
2.1 Centralized Approaches
7(1)
2.2 Agent-based Approaches
7(2)
2.2.1 Data-driven Approaches
8(1)
2.2.2 Predictive Approaches
8(1)
2.3 Locomotion Synthesis
9(1)
2.4 Challenges and Proposed Solutions
9(4)
2.4.1 Particle-based Agent Models
10(1)
2.4.2 Decoupling between Steering and Locomotion
10(1)
2.4.3 Generalization and Applicability of Data-driven Approaches
11(2)
3 Footstep-based Navigation and Animation for Crowds
13(10)
3.1 Introduction
13(2)
3.2 Locomotion Model
15(4)
3.2.1 Inverted Pendulum Model
15(1)
3.2.2 Footstep Actions
16(1)
3.2.3 Locomotion Constraints
17(1)
3.2.4 Cost Function
18(1)
3.3 Planning Algorithm
19(1)
3.4 Evaluation
20(3)
3.4.1 Interfacing with Motion Synthesis
22(1)
4 Following Footstep Trajectories in Real Time
23(14)
4.1 Animating from Footsteps
23(1)
4.2 Framework Overview
24(1)
4.3 Footstep-based Locomotion
25(4)
4.3.1 Motion Clip Analysis
25(2)
4.3.2 Footstep and Root Trajectories
27(1)
4.3.3 Online Selection
27(1)
4.3.4 Interpolation
28(1)
4.3.5 Inverse Kinematics
29(1)
4.4 Incorporating Root Movement Fidelity
29(4)
4.5 Results
33(4)
4.5.1 Foot Placement Accuracy
33(1)
4.5.2 Performance
34(3)
5 Context-sensitive Data-driven Crowd Simulation
37(12)
5.1 Steering in Context
37(1)
5.2 Steering Contexts
38(1)
5.3 Initial Implementation
39(5)
5.3.1 Training Data Generation
39(2)
5.3.2 Oracle Algorithm
41(1)
5.3.3 Decision Trees
42(2)
5.3.4 Steering at Runtime
44(1)
5.4 Results
44(5)
5.4.1 Classifier Accuracy
44(1)
5.4.2 Runtime
45(1)
5.4.3 Collisions
46(3)
6 Conclusion
49(4)
6.1 Footstep-based Collision Avoidance
49(1)
6.2 Footstep-based Locomotion
49(1)
6.3 Context-based Steering
50(3)
Part II Multi-agent Navigation 53(38)
7 Background
55(4)
7.1 Navigation Meshes
55(2)
7.2 Planning
57(2)
8 Navigation Meshes
59(16)
8.1 NavMeshes from 3D Geometry: NEOGEN
59(10)
8.1.1 GPU Coarse Voxelization
60(2)
8.1.2 Layer Extraction and Labeling
62(1)
8.1.3 Layer Refinement
63(4)
8.1.4 NavMesh Generation
67(2)
8.2 Results
69(3)
8.3 Limitations and Discussion
72(3)
9 Multi-domain Planning in Dynamic Environments
75(14)
9.1 Multi-domain Planning
75(1)
9.2 Overview
76(1)
9.3 Planning Domains
77(2)
9.3.1 Multiple Domains of Control
77(2)
9.4 Problem Decomposition and Multi-domain Planning
79(3)
9.4.1 Planning Tasks and Events
81(1)
9.5 Relationship between Domains
82(1)
9.5.1 Domain Mapping
83(1)
9.5.2 Mapping Successive Waypoints to Independent Planning Tasks
83(1)
9.6 Results
83(14)
9.6.1 Comparative Evaluation of Domain Relationships
83(2)
9.6.2 Performance
85(2)
9.6.3 Scenarios
87(2)
10 Conclusion
89(2)
Part III Perception 91(52)
11 Background
93(4)
12 Sound Propagation and Perception for Autonomous Agents
97(20)
12.1 Sound Categorization and Representation
99(6)
12.1.1 Sound Feature Selection and Categorization
100(1)
12.1.2 Sound Packet Representation (SPR)
100(2)
12.1.3 SPR Selection for Hierarchical Cluster Analysis
102(3)
12.2 Sound Packet Propagation
105(4)
12.2.1 Transmission Line Matrix Using Uniform Grids
105(1)
12.2.2 Pre-computation for TLM using a Quad Tree
106(3)
12.3 Sound Perception and Behaviors
109(2)
12.3.1 Effect of Sound Degradation on Perception
109(1)
12.3.2 Hierarchical Sound Perception Model
109(2)
12.3.3 Sound Attention and Behavior Model
111(1)
12.4 Experiment Results
111(6)
12.4.1 Applications
114(3)
13 Multi-sense Attention for Autonomous Agents
117(14)
13.1 Introduction
117(2)
13.2 Methodology
119(3)
13.2.1 Object and Action Representations
119(1)
13.2.2 Sense Preprocessing
119(1)
13.2.3 Sensing
120(2)
13.3 Hierarchical Aggregate Clustering
122(5)
13.3.1 Environment-centric Clustering
122(1)
13.3.2 Agent-centric Clustering
123(3)
13.3.3 Aggregate Properties
126(1)
13.4 Analysis and Results
127(4)
14 Semantics in Virtual Environments
131(10)
14.1 Incorporating Semantics
131(1)
14.1.1 Lexical Databases
132(1)
14.1.2 Modularized Smart Objects
132(1)
14.2 Semantic Generation
132(7)
14.2.1 Hierarchy Generation
133(3)
14.2.2 Semantic Modularization
136(1)
14.2.3 Runtime Performance
136(3)
14.3 Limitations
139(2)
15 Conclusion
141(2)
Part IV Agent-Object Interactions and Crowd Heterogeneity 143(32)
16 Background
145(4)
17 Parameterized Memory Models
149(10)
17.1 Memory System
150(3)
17.1.1 Memory Representation
150(1)
17.1.2 Sensory Memory
151(1)
17.1.3 Working Memory
151(1)
17.1.4 Long-term Memory
152(1)
17.2 Example and Analysis
153(3)
17.3 Future Work
156(3)
18 Individual Differences
159(14)
18.1 Personality
159(7)
18.1.1 Personality-to-Behavior Mapping
160(3)
18.1.2 User Studies on Personality
163(3)
18.2 Roles and Needs
166(15)
18.2.1 Approach
166(2)
18.2.2 Implementation
168(5)
19 Conclusion
173(2)
Part V Behavior and Narrative 175(44)
20 Background
177(4)
21 An Open Source Platform for Authoring Functional Crowds
181(18)
21.1 ADAPT
181(1)
21.2 Framework
182(3)
21.2.1 Full-body Character Control
182(2)
21.2.2 Steering and Path-finding
184(1)
21.2.3 Behavior
184(1)
21.3 Shadows in Full-body Character Animation
185(6)
21.3.1 Choreographers
185(1)
21.3.2 The Coordinator
186(2)
21.3.3 Using Choreographers and the Coordinator
188(1)
21.3.4 Example Choreographers
189(2)
21.4 Character Behavior
191(3)
21.4.1 The ADAPT Character Stack
191(1)
21.4.2 Body Capabilities
192(2)
21.5 Character Interactions
194(2)
21.5.1 Characters Interacting with Each Other
194(2)
21.5.2 Characters Interacting with the Environment
196(1)
21.6 Results
196(3)
21.6.1 Multi-actor Simulations
196(1)
21.6.2 Computational Performance
197(2)
22 Event-centric Planning for Narrative Synthesis
199(16)
22.1 Problem Domain and Formulation
200(4)
22.1.1 State Space
201(1)
22.1.2 Action Space
202(1)
22.1.3 Goal Specification
203(1)
22.2 Planning in Event Space
204(1)
22.3 Runtime and Simulation
204(3)
22.3.1 Event Loading and Dispatch
204(1)
22.3.2 Handling Dynamic World Changes
205(2)
22.3.3 Intelligent Ambient Character Behavior
207(1)
22.4 Results
207(8)
22.4.1 Environment Design
208(1)
22.4.2 Object State Description
208(1)
22.4.3 Authored Events
209(1)
22.4.4 Generated Narrative
209(3)
22.4.5 Reacting to User Intervention
212(3)
23 Conclusion
215(2)
24 Epilogue
217(2)
Bibliography 219(28)
Authors' Biographies 247