1. Turning to the Masters: Motion Capturing Cartoons
Christopher Bregler, Lorie Loeb, Erika Chuang, Hrishi Deshpande
SIGGRAPH ’02

2. Motivation Animation has two dimensions: Visual Style Motion Style
Style of Drawing or Model
Rendering etc
Motion Style
How Characters Move
Amount of Exaggeration
Use of Cartoon Physics

3. Examples

4. Motivation Motion Capture restricted to a very small region (Green)
Try to isolate the motion style of cartoon animation and apply it to different visual styles

5. Challenges: Capture Start with a 2-dimensional animated video
Cartoon characters have no markers
Low Frame Rate makes tracking difficult
Identifying limb locations in cartoon characters is difficult
Cartoon Objects undergo large degrees of non-rigid deformation throughout the sequence

6. Challenges: Retargeting
Most retargeting techniques based on skeletal models
Can capture only 2D information. Retargeting domain may be 3D models

7. Main Idea: Parameterize cartoon motion with a combination of affine-transformations and key-weight vectors.
Claim: Describe a wide variety of motion and non-rigid shape deformation.

8. Affine Deformations

9. Affine Deformations Important part of cartoon motion comes from
Velocity of entire body
Stretch and Squash in different directions
S is a 3xN shape matrix encoding N points in homogenous form,
si=[xi,yi,1]T
The ball shape V(t) at time-frame t is defined with the equation on left

10. Key-Shape Deformations
For more complicated motion use a set of characteristic key-shapes Si . The model then extends to

11. Extended Linear Warping Space
In-Between shapes produce undesirable visual artifacts
Can be avoided by using a large number of intermediate shapes
Can use non-linear interpolating functions but then inverse calculation is non-trivial

12. Extended Linear Warping Space..
Solution:
For every combination of hand-picked key-shapes generate M in-between key-shapes.
For K shapes: (K-1)*(K-2)*M shapes now
Use PCA to give:
Mean shape M
Eigen Vectors E1…EL.
Using Sl=M+El and Sl+1=M gives the extended linear space

13. Contour Capture
Given sequence of cartoon contours: V(1)…V(t) and labeled key-shapes S1….Sk need to find affine matrix and the weights

14. Contour Capture Done by a two-step process.
Estimate the affine parameters.
Estimate the key-weights
Iterate.
Some constraints include all key-shapes adding up to 1. Reason: Good at interpolation and bad at extrapolation

15. Video Capture Input: Sequence of images instead of contours
Extend the model to directly model image pixel variations.

16. Video Capture: Affine case
S2xN=[s1,s2…sN] :contains (x,y) coordinates of all pixels in cartoon image region
I(si): intensity at pixel si
Io: Image Template (Base Key-Shape)

17. Approximation

18. Video Capture: Affine and Key-Shape
Io: Was the image template (Base Key-Shape) in affine case
Replace it with linear combination of key-shapes.
The error function thus becomes:

19. Results from Video-Capture

20. Results (contd..)

21. Retargeting Motion Key-Shape based
Correspondence also specified between control points etc:.

22. Results
Video

23. Pros and Cons Advantages Disadvantages
Attempts the problem of cartoon capture
Motion Style that is expressive is taken into consideration
Adopts simple models and hence can be fast
Disadvantages
Adopts very simple models.
Input Key-Shapes and Correspondences need to be provided.
Adopts interpolation
Retargeting to 3D not handled completely
Most Importantly: No temporal constraint used. Hence jitter would be a major problem.