Download Animation of Large Crowd

Animating Real-Time Crowds
Outline
• Introduction
• Description of Level of Detail
• Level of Detail in…
– (agent behavior)
• …sensing
• …behavioral architectures
– (agent motion)
• …motion simulation
• …animation cache
• …skeletal pruning
– (rendering)
• …geometry
• …replication
• References
Introduction
• An agent-based simulation models the
movements of independent entities in an
environment.
– The agent is responsible for controlling a single entity
in the simulation.
– The agent is responsible for setting goals, actions to
achieve those goals, and react to external
environmental stimuli.
• This method has been used to model flocks of
birds, schools of fish, herds of animals, and
crowds of people.
• This presentation will focus on modeling
humanoid crowds in real-time.
Introduction
• The primary difficulty in a real-time agentbased simulation is scalability.
– Agent-agent interactions can be O(n2).
• Sensing: Who is near me?
• Behaviors / Response to Stimuli: What should I
do?
• Very application specific.
– Animating an articulated human model is
expensive.
• Both areas will be addressed in this
presentation.
Level of Detail
• Level of Detail (LOD) is a common technique
used to approximate the solution to a complex
system.
• LOD is commonly driven by an observer’s
location.
– Elements near the observer are simulated with a high
degree of fidelity, while farther elements are simulated
with any number of LODs.
• LOD may be applied to sensing, behavior
simulation, motion of articulated models, and
model geometry.
LOD and Agent Behavior
Sensing
• Agents use sensing to gather information about
their world/situation.
– Provides input to a behavior model which in-turn
outputs actions/responses.
• Unlike in the real world, agents can be
omniscient.
– Agents may know the locations of all others.
– Agents may know the map of the entire environment.
– Agents may communicate with all others, regardless
of location.
• The problem of sensing is in filtering all this data.
Sensing (2)
• Sensors are used by agents to filter the
world data.
– Ideally, an agent should have visual, auditory,
and tactile senses.
• No need for taste and smell– at least just yet.
– Vision is only commonly available.
Sensing (3)
• Vision can be implemented in several ways:
– Rendering-Based: Images are rendered from the agent’s point of
view in false color.
• Object identification.
• Z-buffer for collision avoidance.
• Not scalable to many agents in real-time.
– Database-Access: Local neighborhood is queried for information.
• Most common.
• These methods are generally fast and can be tuned for level of
detail.
• Example:
– Kd-tree database to find nearby agents and obstacles.
» LOD: Limit search radius. Cap query results.
– Search a patch of a raster grid to find nearby agents and obstacles.
» LOD: Raster grid cell size. Size of patch. Cap query results.
– Neither allow for long-range vision.
Behavioral Architectures
• Real-time crowds nearly always require an
interactive element (otherwise there would
be no real-time requirement).
• Support for interactivity and its impact on
agent behavior (user  agent and agent
 agent) drives much of the system’s
software architecture.
Behavioral Architectures (2)
•
The typical agent cycle looks like
[Niederberger]:
1. Sense
2. Think
3. Act
•
•
The first two steps can be difficult to perform
efficiently if each agent interrogates its
environment individually.
Shared sensing, thinking, and acting can be
used to reduce computational cost if the agent
is a part of a group.
– A group generalization may be thought as a LOD.
Behavioral Architectures (3)
• Shared Knowledge Base [Niederberger]:
– All world data is stored in a common knowledge base
(KB) shared by all agents.
•
•
•
•
Agent Positional Data
Sensors
Situations
Agent Groups
– Topics within the KB are keyed with various topic IDs.
– Each agent has KB keys for accessing data.
• Agent Keys, Group Keys, Sensor Keys
Behavioral Architectures (4)
• Advantages:
– Agents can quickly
access group
awareness.
– Sensors may be
shared among agents.
– Very easy to
multithread the “think”
phase since it is “readonly” on the KB.
Behavioral Architectures (5)
• Group behaviors are
realized through
hierarchical relations
between agents.
– A typical hierarchy is a
leader/follower model.
• Heavy “thinking” (such as
path planning) is only
performed by the leader.
– Followers only need to
keep up and follow signals
raised by their leader.
Behavioral Architectures (6)
• Accelerating the Think phase:
– Given a hard time limit (say 10ms) to spend
on all agent processing, the simulation loop
only Thinks for a subset of agents (scheduled
using a Round Robin (2003) or Priority Queue
(2005)).
• LOD may be used to allocate time slice (2005).
– Agents may give up slice to group leader if slice is too
small to be useful.
• Skipped agents continue to perform their previous
actions.
Behavioral Architectures (7)
LOD scheduling priorities are allocated by visibility and distance.
Behavioral Architectures (8)
Images show
agent autonomy.
Agents in the
field of view and
near the camera
act
independently.
Agents off
camera are
controlled by
group leaders.
Behavioral Architectures (9)
• An aside about Thinking:
– Thinking makes up the agent’s behavior model and
can be very simple or very complex.
– Simple: Finite State Machine
– Moderately Complex: Fuzzy Logic
– Complex: Neural Network, Bayesian Network
• It all depends upon the application and
behaviors trying to be achieved.
– Real-Time crowd sim will typically use a FSM.
– Niederberger explores iterative algorithms for timeallocated thinking. Allows for a useful partial solution
to be computed in allotted time that may be improved
upon in the next time slice.
Behavioral Architectures (10)
• Accelerating the Act phase:
– A reduced set of actions can also be executed given
time constraints.
– Actions are prioritized in a queue.
• Simulation tries to execute all queued actions, but may have
to exit early if the time limit is exceeded.
– Reactive actions (such as flee a predator) are given
priority over less important actions such as follow the
leader.
Behavioral Architectures (11)
• Summary of performance advantages of
Niederberger’s architecture:
– Shared knowledge base.
• Data quickly available.
• Easy to multithread.
– Hierarchical group organization allows
“heavy” processing to be done by group
leaders.
– LOD-driven scheduling of behavioral updates.
– Prioritization of actions.
Behavioral Architectures (12)
• Hierarchical group structures are a
common way to reduce the “heavy”
computational costs of the crowd
simulation.
• Musse and Thalmann’s ViCrowds [Musse]:
– Inherited state and behaviors (instead of
shared KB).
– Group splitting/merging.
– Group dominated by single leader or more
complex sociological models.
LOD and Agent Motion
Motion Simulation
• Thus far in this class we have already
discussed several methods for animating
articulated models.
– Motion Controllers
– Inverse Kinematics
– Motion Capture / Animator Key-Frames
• Many of these are very computationally
expensive.
Motion Simulation (2)
• Using LOD, it is conceivable to create a
crowd simulation where agents near the
observer are animated with high quality
techniques while farther agents use more
simplified methods.
• Switching between the appropriate LODs
can be challenging, but may be addressed
with the appropriate architecture.
Motion Simulation (3)
• Giang et al. devise a
architecture where an
“LOD Resolver”
sends LOD requests
to a “Motion
Controller”.
• The Motion Controller
invokes the
appropriate animation
method.
Motion Simulation (4)
• The Motion Controller
contains different submodules used for the
various animation
techniques.
• The “Animation
Cache” stores current
“in-flight” animations.
Animation Cache
(Not Giang’s animation cache)
• Articulated models are typically have a skeletal structure
(made up of bones) covered with a skin (made up of
mesh vertices).
• Animations are generated by moving the model’s bones.
• A vertex is transformed with respect to the bone
configuration by the following equation:
n
v(t )   X it X i ref v ref
i 1
Xti is the global transform of bone i at time t.
X-refi is the inverse global transform of the bone in the reference position.
vref is the vertex in the reference position.
• Vertex positions are computed though the application of hierarchical
transformations starting from the root bone.
Animation Cache (2)
• Agents far from the observer may be animated
at a low frequency (much lower than rendered
frame rate).
– Example: Thalmann’s Virtual Heritage used 4Hz for
distant agents and 50Hz for nearby ones.
• A single pose may be reused between several
frames.
• The animation cache is this reused pose.
• Generating the cache:
– Interpolate between two key frames.
– Apply vertex skinning transformations.
– Store result as the cache.
Skeletal Pruning
• There is another LOD technique that uses
the agent’s skeleton.
• The hierarchical bone transformations can
be costly for highly articulated models.
• A cost savings would be realized if the
number of bones could be reduced.
Skeletal Pruning (2)
• By “fusing” bones together, a sub-hierarchy of
the skeletal structure can be used to transform
all vertices within the sub-hierarchy.
– Fewer rotation interpolations.
– Fewer matrix multiplications.
• Costs:
– On a new level-of-detail, bone assignments of
vertices must be recomputed with respect to the fused
bone.
LOD in Rendering
LOD in Rendering
• While not explicitly related to simulation,
fast rendering allows for more time to be
spent performing simulation.
• There are several techniques that are
commonly used to speed up crowd
rendering.
Geometric LOD
• Geometric & Animation LOD
– Reduce the number of polygons in the model at
different LOD steps.
– At a certain LOD, stop animating bones of figure and
use static-keyframe meshes instead.
• May continue to reduce polygons in the model.
– At the farthest LOD, stop using static meshes and use
2D billboards.
• May be a simple sprite animation.
• Or more complex “polyposter” [Kavan] which is a collection of
2D deformable textured polygons.
Geometric LOD (2)
Geometric LOD (3)
A 2D billboard from
different views can be
generated dynamically.
Billboards can be used to dramatically
increase crowd size.
Geometric LOD (4)
Polyposter approximation of a full mesh animation.
Replication
• Replication
– Transformed agent meshes from an animation can be shared between
agents.
• For example, an two agents may share the same model at the same
animation frame.
– The model is replicated for both agents.
– Model texturing may be altered for each instance. (Example: Different
color of clothes.)
– Commonly used for spectator crowds.
References
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Aubel, A., Boulic, R., Thalmann, D.: Real-time display of virtual humans: Levels of detail and imposters. IEEE
Transactions on Circuits and Systems for Video Technology 10(2), 207-217 (2000)
Azahar, M. A., Sunar, M. S., Daman, D., and Bade, A. 2008. Survey on Real-Time Crowds Simulation. In
Proceedings of the 3rd international Conference on Technologies For E-Learning and Digital Entertainment
(Nanjing, China, June 25 - 27, 2008). Z. Pan, X. Zhang, A. Rhalibi, W. Woo, and Y. Li, Eds. Lecture Notes In
Computer Science, vol. 5093. Springer-Verlag, Berlin, Heidelberg, 573-580.
de Heras, P., Schertenleib, S., Maim, J., Thalmann, D.: Realtime shader rendering for crowds in virtual heritage.
In: Proc. 6th International Symposium on Virtual Reality, Archaeology and Cultural Heritage (VAST 2005) (2005)
Giang, T., Mooney, R., Peters, C. O’Sullivan, C.: ALOHA: Adaptive Level Of Detail for Human Animation: Towards
a new framework. Eurographics 2000 short paper proceedings. pp. 71-77 (2000)
Hery, C., Lippman, S., Ono, H., Regelous, S., Sutton, S., Thalmann, D.: Crowd and Group Animation, SIGGRAPH
Course (2004)
Kavan, L., Dobbyn, S., Collins, S., Žára, J., and O'Sullivan, C. 2008. Polypostors: 2D polygonal impostors for 3D
crowds. In Proceedings of the 2008 Symposium on interactive 3D Graphics and Games (Redwood City, California,
February 15 - 17, 2008). I3D '08. ACM, New York, NY, 149-155.
Musse, S.R., Thalman, D.: A hierarchical model for real time simulation of virtual human crowds. IEEE
Transactions on Visualization and Computer Graphics 7(2), 152-164 (2001)
Niederberger, C., Gross, M.: Hierarchical and heterogeneous reactive agents for real-time applications. Computer
Graphics Forum 22(3) (Proc. Eurographics 2003) (2003)
Niederberger, C. and Gross, M. 2005. Level-of-detail for cognitive real-time characters. Vis. Comput. 21, 3 (Apr.
2005), 188-202.
Ulicnyn, B. Thalmann, D.: Towards interactive real-time crowd behavior simulation. Computer Graphics Forum
21(4), 767-775 (2002)
Ulicny B., de Heras Ciechomski P., Musse S. R., & Thalmann D.: State-of-the-Art: Real-time Crowd Simulation. In
EuroGraphics 2006 Course on Populating Virtual Environments with Crowds.
Unattributed slide from SIGGRAPH Asia 2008 course.