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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 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 Tested on SGI InfiniteReality2 Click and drag goals and obstacles
1 2
3 4
A character exploring unknown mazes
Conclusions Efficient in storage and update
times Flexible Bottlenecks at synthetic vision
model (double rendering)