CS-184: Computer Graphics Lecture #19: Motion Capture ! ! ! Prof. - - PDF document

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CS-184: Computer Graphics Lecture #19: Motion Capture ! ! ! Prof. - - PDF document

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CS-184: Computer Graphics Lecture #19: Motion Capture ! ! ! Prof. James OBrien ! University of California, Berkeley ! ! ! V2015-S-18-1.0 1 18-MoCap.key - April 8, 2015 Today Motion Capture 2 2 18-MoCap.key - April 8, 2015 Motion

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CS-184: Computer Graphics

Lecture #19: Motion Capture!

! !

  • Prof. James O’Brien!

University of California, Berkeley!

! !

V2015-S-18-1.0

1 18-MoCap.key - April 8, 2015 2

Today

  • Motion Capture

2 18-MoCap.key - April 8, 2015

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Motion Capture

  • Record motion from physical objects!
  • Use motion to animate virtual objects

Simplified Pipeline:

Setup and calibrate equipment Record performance Process motion data Generate animation

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Basic Pipeline

From Rose, et al., 1998

Setup Record Process Animatio n

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What types of objects?

  • Human, whole body!
  • Portions of body!
  • Facial animation!
  • Animals!
  • Puppets!
  • Other objects

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Capture Equipment

  • Passive Optical!
  • Reflective markers!
  • IR (typically) illumination!
  • Special cameras !
  • Fast, high res., filters!
  • Triangulate for positions

Images from Motion Analysis

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Capture Equipment

  • Passive Optical Advantages!
  • Accurate!
  • May use many markers!
  • No cables!
  • High frequency!
  • Disadvantages!
  • Requires lots of processing !
  • Expensive systems!
  • Occlusions!
  • Marker swap !
  • Lighting / camera limitations

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Capture Equipment

  • Active Optical !
  • Similar to passive but uses LEDs!
  • Blink IDs, no marker swap!
  • Number of markers trades off w/ frame rate

Phoenix Technology Phase Space

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Capture Equipment

  • Magnetic Trackers!
  • Transmitter emits field!
  • Trackers sense field!
  • Trackers report position and orientation

Control May be wireless

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Capture Equipment

  • Electromagnetic Advantages!
  • 6 DOF data!
  • No occlusions!
  • Less post processing!
  • Cheaper than optical!
  • Disadvantages!
  • Cables!
  • Problems with metal objects!
  • Low(er) frequency!
  • Limited range!
  • Limited number of trackers

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Capture Equipment

  • Electromechanical

Analogus

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Capture Equipment

  • Puppets

Digital Image Design

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Performance Capture

  • Many studios regard Motion Capture as evil!
  • Synonymous with low quality motion!
  • No directive / creative control!
  • Cheap!
  • Performance Capture is different!
  • Use mocap device as an expressive input device!
  • Similar to digital music and MIDI keyboards

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Manipulating Motion Data

  • Basic tasks!
  • Adjusting!
  • Blending!
  • Transitioning!
  • Retargeting!
  • Building graphs

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Nature of Motion Data

Witkin and Popovic, 1995

Subset of motion curves from captured walking motion.

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Adjusting

  • IK on single frames will not work

Gleicher, SIGGRAPH 98

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Adjusting

  • Define desired motion function in parts

Result after adjustment Inital sampled data Adjustment

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Adjusting

  • Select adjustment function from “some nice space”!
  • Example C2 B-splines!
  • Spread modification over reasonable period of time!
  • User selects support radius

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Adjusting

Witkin and Popovic SIGGRAPH 95

IK uses control points of the B- spline now!

!

Example:! position racket! fix right foot! fix left toes! balance

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Adjusting

Witkin and Popovic SIGGRAPH 95

What if adjustment periods overlap?

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Blending

  • Given two motions make a motion that combines qualities
  • f both!

! !

  • Assume same DOFs!
  • Assume same parameter mappings

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Blending

  • Consider blending slow-walk and fast-walk

Bruderlin and Williams, SIGGRAPH 95

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Blending

  • Define timewarp functions to align features in motion

Normalized time is w

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Blending

  • Blend in normalized time!

! !

  • Blend playback rate

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Blending

  • Blending may still break features in original motions

Touchdown for Run Touchdown for Walk Blend misses ground and floats

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Touchdown for Run Touchdown for Walk

Blending

  • Add explicit constrains to key points!
  • Enforce with IK over time

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Blending / Adjustment

  • Short edits will tend to look acceptable!
  • Longer ones will often exhibit problems!
  • Optimize to improve blends / adjustments!
  • Add quality metric on adjustment!
  • Minimize accelerations / torques!
  • Explicit smoothness constraints!
  • Other criteria...

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Multivariate Blending

  • Extend blending to multivariate interpolation

"Speed"

“Speed” “Happiness”

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"Speed"

If we have other examples place them in the space also

Multivariate Blending

  • Extend blending to multivariate interpolation

“Speed” “Happiness”

Use standard scattered-data interpolation methods

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Transitions

  • Transition from one motion to another

Perform blend in overlap region

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Cyclification

  • Special case of transitioning!
  • Both motions are the same!
  • Need to modify beginning and end of a motion

simultaneously

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Transition Graphs

Flip Stand Run Walk Sit Trip Dance

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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

Flip Stand Run Walk Sit Trip Dance

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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

  • Random walks!
  • Start in some part of the graph and randomly make transitions!
  • Avoid dead ends!
  • Useful for “idling” behaviors !
  • Transitions!
  • Use blending algorithm

Domain of smoothing Smoothed Signal

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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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Typical Motion Graph

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Recorded Time

Walking #1 Running Idle Walking #2 Fall down Punches

Finite number of states Cloth is hysteretic

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Naïve Precomputation

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Graph Unrolling

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Graph Unrolling

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Graph Unrolling

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100K frames 5000 hours compute 330 GB

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Graph Unrolling

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100K frames 5000 hours compute 330 GB

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Precomputed Cloth

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Wrong inset due to time constraints.! Really it works. Trust me!

72 MB Compressed Laptop 60 fps Low CPU load

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Precomputed Cloth

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Precomputed Simulation

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  • No significant CPU load at runtime!
  • Decouples quality from runtime cost!
  • No new data at runtime!
  • Simulation can’t crash application!
  • All motion can be inspected/edited!
  • Allows QA and art direction of simulations !
  • Extend to other types of simulation?!
  • Dynamic variations?

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Suggested Reading

  • Fourier principles for emotion-based human figure animation, Unuma, Anjyo, and

Takeuchi, SIGGRAPH 95!

  • Motion signal processing, Bruderlin and Williams, SIGGRAPH 95!
  • Motion warping, Witkin and Popovic, SIGGRAPH 95!
  • Efficient generation of motion transitions using spacetime constrains, Rose et al.,

SIGGRAPH 96!

  • Retargeting motion to new characters, Gleicher, SIGGRAPH 98!
  • Verbs and adverbs: Multidimensional motion interpolation, Rose, Cohen, and

Bodenheimer, IEEE: Computer Graphics and Applications, v. 18, no. 5, 1998

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Suggested Reading

  • Retargeting motion to new characters, Gleicher, SIGGRAPH 98!
  • Footskate Cleanup for Motion Capture Editing, Kovar, Schreiner, and Gleicher, SCA 2002. !
  • Interactive Motion Generation from Examples, Arikan and Forsyth, SIGGRAPH 2002.!
  • Motion Synthesis from Annotations, Arikan, Forsyth, and O'Brien, SIGGRAPH 2003.!
  • Pushing People Around, Arikan, Forsyth, and O'Brien, unpublished.!
  • Automatic Joint Parameter Estimation from Magnetic Motion Capture Data, O'Brien,

Bodenheimer, Brostow, and Hodgins, GI 2000.!

  • Skeletal Parameter Estimation from Optical Motion Capture Data, Kirk, O'Brien, and

Forsyth, CVPR 2005.!

  • Perception of Human Motion with Different Geometric Models, Hodgins, O'Brien, and

Tumblin, IEEE: TVCG 1998.

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