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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,

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Presentation on theme: "CS-378: Game Technology Lecture #15.5: Physically Based Simulation Prof. Okan Arikan University of Texas, Austin Thanks to James O’Brien, Steve Chenney,"— Presentation transcript:

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, Zoran Popovic, Jessica Hodgins V2005-08-1.1

2 2 Spring-mass Systems Deformable objects Cloth Hair Rigid Body Simulation Hard contacts Rag dolls ? Game Physics Hitman

3 How about cars ? Crashday

4 Physics Alternatives Motion Capture

5 5 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

6 6 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

7 7 Motion Graphs Compute similarity metric between all pairs of frames Maybe expensive Preprocessing step There may be too many good edges

8 8 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

9 9 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

10 10 Motion Graphs

11 11 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

12 12 Graphs with Annotations

13 13 Supplementing w/ Simulation

14 14 Retargeting Examples

15 15 Footskate Cleanup

16 16 Auto Calibration Skeletons constrain subjects motion Recorded motion retains evidence of constraints Magnetic system yield simple linear constraints Optical are nonlinear

17 17 Perception Issues Motion can be perceived independent of geometry “Biological motion stimuli” tests But geometry does impact motion perception

18 18 Perception Issues Hodgins, O’Brien, and Tumblin, 1998

19 Compression Issues


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