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)