CS-378: Game Technology Lecture #15.5: Physically Based Simulation Prof. Okan Arikan University of...

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CS-378: Game Technology Lecture #15.5: Physically Based Simulation Prof. Okan Arikan University of Texas, Austin Thanks to James O’Brien, Steve Chenney, Zoran Popovic, Jessica Hodgins V2005-08-1.1

Transcript of CS-378: Game Technology Lecture #15.5: Physically Based Simulation Prof. Okan Arikan University of...

Page 1: CS-378: Game Technology Lecture #15.5: Physically Based Simulation Prof. Okan Arikan University of Texas, Austin Thanks to James O’Brien, Steve Chenney,

CS-378: Game Technology

Lecture #15.5: Physically Based Simulation

Prof. Okan ArikanUniversity of Texas, Austin

Thanks to James O’Brien, Steve Chenney, Zoran Popovic, Jessica HodginsV2005-08-1.1

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Spring-mass Systems

Deformable objects

Cloth

Hair

Rigid Body Simulation

Hard contacts

Rag dolls ?

Game Physics

Hitman

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How about cars ?

Crashday

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Physics Alternatives

Motion Capture

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Motion Graphs

Hand build motion graphs often used in games

Significant amount of work required

Limited transitions by design

Motion graphs can also be built automatically

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Motion Graphs

Similarity metric

Measurement of how similar two frames of motion are

Based on joint angles or point positions

Must include some measure of velocity

Ideally independent of capture setup and skeleton

Capture a “large” database of motions

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Motion Graphs

Compute similarity metric between all pairs of frames

Maybe expensive

Preprocessing step

There may be too many good edges

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Motion Graphs

Random walks

Start in some part of the graph and randomly make transitions

Avoid dead ends

Useful for “idling” behaviors

Transitions

Use blending algorithm we discussed

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Motion graphs

Match imposed requirements

Start at a particular location

End at a particular location

Pass through particular pose

Can be solved using dynamic programing

Efficiency issues may require approximate solution

Notion of “goodness” of a solution

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Motion Graphs

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Graphs with Annotations

Place semantic labels on motions

Example: walking, running, waving, moving-backward

Use include match to desired annotation in goodness

How to place labels automatically?

Statistical classifiers

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Graphs with Annotations

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Supplementing w/ Simulation

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Retargeting Examples

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Footskate Cleanup

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Auto Calibration

Skeletons constrain subjects motion

Recorded motion retains evidence of constraints

Magnetic system yield simple linear constraints

Optical are nonlinear

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Perception Issues

Motion can be perceived independent of geometry

“Biological motion stimuli” tests

But geometry does impact motion perception

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Compression Issues