CS计算机代考程序代写 GPU Crowd Animation

Crowd Animation
Michael Andereck CSE 888 March 7, 2008

• Particle systems
• Flocking systems
• Behavioral systems

Particle Systems
• Tens of Thousands of members
• No intelligence
• Respond to global forces, including collision reaction

Flocking Systems
• Thousands of members
• Limited intelligence
• Some physical basis
• Collision avoidance
• Local control system

Flocks, Herds, and Schools:
A Distributed Behavioral Model
• “Boids” given simple behavioral constraints:
– Separation – Alignment – Cohesion
• Often cited as one of the first examples of flocking and emergent behavior

Behavioral Systems
• Tens to thousands of members
• Each member is highly intelligent
• Collision avoidance
• Behavior based on rules

Applications of Crowd Models
• Computer animation
• Games, interactive graphics, and virtual reality
• Robotics
• Aerospace
• Education
• Artificial life
• Art
• Biology
• Physics
• Other areas

Hierarchical Model for Real Time Simulation of Virtual Human Crowds
• Crowds are modeled at varying hierarchical levels:
– Crowd – Groups – Agents
• Behavior is controlled by external control, scripted control, and at a low level, the innate behavior

• Soraia Raupp Musse and Daniel Thalmann, 2001

Densely Populated Urban Environments
• Focus is on rendering large crowds with robust collision detection, realistic shading, culling, and pedestrian movement
• Yiorgos Chrysanthou and Franco Tecchia, 2001
Figure 2. Particles avoid walls but climb over the smaller steps

Scalable Behaviors for Crowd Simulation
• Allows increase in crowd behavioral complexity without a corresponding increase in agent complexity
• Users can dynamically specify crowd behaviors
• Agents have a situation-based control structure
• Mankyu Sung, Michael Gleicher, Stephen Chenney, 2004
Figure 5: Left: Spatial situations can be easily set by drawing
directly on the environment. Situation composition can
be specied by overlaying regions. Right: Non- spatial situation
can be set on the crowd by grouping participants.

Autonomous Pedestrians
• Tackles the open problem of emulating real pedestrians in urban environments
• Motions controlled at different levels
– Reactive behaviors
– Navigationalandmotivationalbehaviors – Otherinterestingbehaviors
• Information stored in mental states
• Wei Shao and Demetri Terzopoulos, 2005

Fast and Accurate Goal-Directed Motion Synthesis for Crowds
• Limit characters to specific postitions
• Generate a space of possible actions with a motion graph
which is searched to find the appropriate action
• Mankyu Sung, Lucas Kovar and Michael Gleicher, 2005

Modeling Behavior in a School of Fish by Fast Synthetic Distributed Vision
• Uses distributed vision rays to lower the cost of computing the fish’s perception at each step.
• Adriano Rinaldi, Alessio Malizia, Rick Parent, 2006

Continuum Crowds
• Environment creates a potential field based on density and topographical speed
• Individual direction based on goals, speed, and discomfort
• New characters can be added at low cost

Imposters and pseudo-instancing for the GPU
• Goal: to use the GPU to render as many characters as possible at interactive speed
• Imposters and pseudo- instancing are used to simplify rendering where applicable
• 2^20 characters at 5 fps

Crowd Creation Pipeline for Games
• Realistic crowds are still too expensive for realistic gaming
• Combine geometry and imposter approach for faster rendering
• Also include pre-simulated cloth sequences
• Model prepping
• Exporting the data
• Rendering
• R. McDonnell, S. Dobbyn and C. O’Sullivan, 2006

A Conceptual Framework for Modeling Crowd Behavior
• Priya Dey and David Roberts in SIGSIM 2007

• A context-aware agent which is able to dynamically change its desires and needs

• • • •
Controlling Individual Agents in High- Density Crowd Simulation
Focus on global wayfinding and local motion
Take into account agent personalities
Line forming and character interaction, including pushing N. Pelechano, J.M. Allbeck and N.I. Badler, 2007

Massive Software
• The most widely used package for large-scale crowd simulations
• Used in The Lord of the Rings, The Chronicles of Narnia, I, Robot, Happy Feet and others
• Developed by Stephen Regelous

Future Work
• Larger scale crowds at interactive speeds
• Add a wider variety of behaviors for the crowd
• Add more dynamic control
• Balance between agent autonomy and animator control

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