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