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WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized Delogne- Kåsa

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Page 1: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

WSCG’07

Jonathan Kipling Knight

1 Feb 2007

Copyright © Jonathan Kipling Knight 2007

Fast Skeleton Estimation from Motion Capture Data using Generalized Delogne-Kåsa

Page 2: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 2

Synopsis

• Purpose

• Motion Capture

• Skeleton Formation

• Closed Form Solution

• Conclusion

Page 3: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 3

Purpose

• Draw an articulated framework of solid segments connected by joints.

• Fastest possible solution from motion capture data

Page 4: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 4

Motion Capture

• Magnetic Trackers• Position and Orientation

• Marker Reflectors• Position if in view

• Figure Tracking• Computer vision and image analysis

Page 5: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 5

Motion Capture Session

CMU 05-05

QuickTime™ and aYUV420 codec decompressor

are needed to see this picture.

Page 6: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 6

Producing a Skeleton

• Single Time Frame• Produce position, size and orientation of

each segment• Markers are fixed 3D positions on segment• Orientation is included with magnetic

trackers• Draw lines between rotation points

Page 7: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 7

Time Slice

Page 8: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 8

Inverse Kinematics

• What joint angles are needed to get to next position and orientation?

• Good for filling in large frame gaps

• Sometimes more than one answer

Page 9: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 9

Inverse Kinematics Example

CMU 05-05

QuickTime™ and a decompressor

are needed to see this picture.

Page 10: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 10

Closed Form Solution

• Find centers of rotation for each segment

• Each frame independently drawn

• No iterations

• Quick solution

Page 11: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 11

Segment Tree

• Root segment usually hips

• Leaf segments hands, head and feet

• No loops

Root

Leaves

Page 12: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 12

Solve Sphere at Each Joint

• One marker on child produces sphere around joint relative to parent

• Must know orientation of parent• 1-3 markers needed or• Magnetic trackers

Page 13: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 13

Three Point Orientation

• Three Orthogonal Axes

ˆ z =r p 3 −

r p 1( ) × ˆ x

r p 3 −

r p 1( ) × ˆ x

ˆ x =r p 2 −

r p 1r

p 2 −r p 1

ˆ y = ˆ z × ˆ x

Page 14: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 14

Two Point Orientation

• Three Orthogonal Axes

• Substitute center of rotation

ˆ z =r p 2 −

r c ( ) × ˆ x

r p 2 −

r c ( ) × ˆ x

ˆ x =r p 1 −

r c

r p 1 −

r c

ˆ y = ˆ z × ˆ x

Page 15: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 15

One Point Orientation

• Three Orthogonal Axes

• Substitute center of rotation and constant axis

ˆ z =r p 1 −

r c ( ) × ˆ x

r p 1 −

r c ( ) × ˆ x

ˆ x = ˆ n

ˆ y = ˆ z × ˆ x

Page 16: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 16

Extra Information

• Center of Rotation is available from previously calculated segment

• Constant Axis is available for segments with near cylindrical motion.

Page 17: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 17

Center of Sphere

• Generalized Delogne-Kåsa Method for points on a hypersphere xi

rc = x + 1

2C−1S

C = 1N−1 x i − x ( ) x i − x ( )

T

i=1

N

S = 1N−1 x i − x ( ) x i − x ( )

Tx i − x ( )

i=1

N

x = 1N x i

i=1

N

Page 18: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 18

Constant Axis

• Test by condition number or determinant of C

• Null vector is axis of cylinder motion

• Center of circle €

C ≈ 0

C ˆ n = 0

r′ c =

r c + ˆ n ̂ n T x −

r c ( )

Page 19: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 19

GDK Properties

• Closed form solution for any dimension

• Fastest known 26N

• Cholesky inverse of 3x3 matrix

• Biased when partial coverage of sphere

• As accurate as data O()

Page 20: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 20

Marker Requirements

• 3 Markers on root segment of tree

• 1-3 Markers on all other segments

• Segments with 1 Marker should have one degree of freedom (e.g.knee,elbow)

Page 21: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 21

Break Dance

CMU 85-14

QuickTime™ and a decompressor

are needed to see this picture.

Page 22: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 22

Salsa Dance

CMU 60-08

QuickTime™ and a decompressor

are needed to see this picture.

Page 23: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 23

Conclusion

• GDK is fastest available sphere solution 26N

• As accurate as data O()

• 1-3 Marker requirements per segment

• Provides skeleton to attach solid shape

Page 24: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 24

Future Research

• Unbiased version of GDK

• Full analysis of statistical nature

• Condition for acceptable data

Page 25: WSCG’07 Jonathan Kipling Knight 1 Feb 2007 Copyright © Jonathan Kipling Knight 2007 Fast Skeleton Estimation from Motion Capture Data using Generalized

1 Feb 2007 JKK 25

Acknowledgments

• The data used in this project was obtained from mocaps.cs.cmu.edu. The database was created with funding from NSF EIA-0196217.