Turning to the Masters: Motion Capturing Cartoons Christopher Bregler, Lorie Loeb, Erika Chuang, Hrishi Deshpande SIGGRAPH ’02
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Transcript Turning to the Masters: Motion Capturing Cartoons Christopher Bregler, Lorie Loeb, Erika Chuang, Hrishi Deshpande SIGGRAPH ’02
Turning to the Masters:
Motion Capturing Cartoons
Christopher Bregler, Lorie Loeb, Erika
Chuang, Hrishi Deshpande
SIGGRAPH ’02
Motivation
Animation has two
dimensions:
Visual Style
Style of Drawing or
Model
Rendering etc
Motion Style
How Characters
Move
Amount of
Exaggeration
Use of Cartoon
Physics
Examples
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
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
Challenges: Retargeting
Most retargeting techniques based on
skeletal models
Can capture only 2D information. Retargeting
domain may be 3D models
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.
Affine Deformations
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,
The ball shape V(t) at
time-frame t is defined
with the equation on left
si=[xi,yi,1]T
Key-Shape Deformations
For more complicated motion use a set of characteristic
key-shapes Si . The model then extends to
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
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
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
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
Video Capture
Input: Sequence of images instead of contours
Extend the model to directly model image pixel
variations.
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)
Approximation
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:
Results from Video-Capture
Results (contd..)
Retargeting Motion
Key-Shape based
Correspondence also specified between
control points etc:.
Results
Video
Pros and Cons
Advantages
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.