fast synthetic vision, memory, and learning models for virtual humans
Post on 21-Dec-2015
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Fast Synthetic Vision, Memory, and Learning Models for Virtual Humans
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Purpose Model synthetic vision, memory,
and learning Quickly synthesize motion from
goals
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Introduction Virtual robot Combines path planner and
controller Internal record of perceived objects
and states
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Related Work Virtual perception Model information flow to character
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Synthetic Vision Determine what is currently visible
to character Speed & ability to handle dynamic
environments
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Synthetic Vision - cont. Render unlit model of scene from
character’s POV List of visible objects combined
with each object’s location determines observations
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A character in a virtual office
True color False Color
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Internal Representation & Memory Internal model Object geometry from environment
and observed states
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Perception-Based Navigation Character has set M of
observations Observations represented as
(objIDi, Pi, Ti, vi, t) M updated at regular intervals
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Basic sense-plan-control loop (static environments)
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Perception-Based Navigation - cont. Dynamic environments
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Perception-Based Navigation - cont. Problem: Truly missing vs.
obscured Solution: Re-run vision module
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Revised sense-plan-control loop (dynamic environments)
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Learning and Forgetting Temporal models Different memory rules for different
objects (logical or deductive model)
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Experimental Results Tested on SGI InfiniteReality2 Click and drag goals and obstacles
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A character exploring unknown mazes
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Conclusions Efficient in storage and update
times Flexible Bottlenecks at synthetic vision
model (double rendering)