Fast Synthetic Vision, Memory, and
Learning Models for Virtual Humans
Purpose


Model synthetic vision, memory,
and learning
Quickly synthesize motion from
goals
Introduction



Virtual robot
Combines path planner and
controller
Internal record of perceived objects
and states
Related Work


Virtual perception
Model information flow to character
Synthetic Vision


Determine what is currently visible
to character
Speed & ability to handle dynamic
environments
Synthetic Vision - cont.


Render unlit model of scene from
character’s POV
List of visible objects combined
with each object’s location
determines observations
A character in a virtual office
True color
False Color
Internal Representation
& Memory
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
Internal model
Object geometry from environment
and observed states
Perception-Based
Navigation



Character has set M of
observations
Observations represented as
(objIDi, Pi, Ti, vi, t)
M updated at regular intervals
Basic sense-plan-control loop (static environments)
Perception-Based
Navigation - cont.

Dynamic environments
Perception-Based
Navigation - cont.


Problem: Truly missing vs.
obscured
Solution: Re-run vision module
Revised sense-plan-control loop (dynamic environments)
Learning and Forgetting


Temporal models
Different memory rules for different
objects (logical or deductive
model)
Experimental Results
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
Tested on SGI InfiniteReality2
Click and drag goals and obstacles
1
3
2
4
A character exploring unknown mazes
Conclusions
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

Efficient in storage and update
times
Flexible
Bottlenecks at synthetic vision
model (double rendering)