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Fast Synthetic Vision, Memory, and Learning Models for Virtual Humans.

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Presentation on theme: "Fast Synthetic Vision, Memory, and Learning Models for Virtual Humans."— Presentation transcript:

1 Fast Synthetic Vision, Memory, and Learning Models for Virtual Humans

2 Purpose n Model synthetic vision, memory, and learning n Quickly synthesize motion from goals

3 Introduction n Virtual robot n Combines path planner and controller n Internal record of perceived objects and states

4 Related Work n Virtual perception n Model information flow to character

5 Synthetic Vision n Determine what is currently visible to character n Speed & ability to handle dynamic environments

6 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

7 A character in a virtual office True color False Color

8 Internal Representation & Memory n Internal model n Object geometry from environment and observed states

9 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

10 Basic sense-plan-control loop (static environments)

11 Perception-Based Navigation - cont. n Dynamic environments

12 Perception-Based Navigation - cont. n Problem: Truly missing vs. obscured n Solution: Re-run vision module

13 Revised sense-plan-control loop (dynamic environments)

14 Learning and Forgetting n Temporal models n Different memory rules for different objects (logical or deductive model)

15 Experimental Results n Tested on SGI InfiniteReality2 n Click and drag goals and obstacles

16 12 34

17 A character exploring unknown mazes

18 Conclusions n Efficient in storage and update times n Flexible n Bottlenecks at synthetic vision model (double rendering)


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