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Published byJulio Rivero Acuña Modified over 6 years ago
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Turning to the Masters: Motion Capturing Cartoons
Christopher Bregler, Lorie Loeb, Erika Chuang, Hrishi Deshpande SIGGRAPH ’02
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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
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Examples
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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
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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
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Challenges: Retargeting
Most retargeting techniques based on skeletal models Can capture only 2D information. Retargeting domain may be 3D models
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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.
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Affine Deformations
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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
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Key-Shape Deformations
For more complicated motion use a set of characteristic key-shapes Si . The model then extends to
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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
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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
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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
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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
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Video Capture Input: Sequence of images instead of contours
Extend the model to directly model image pixel variations.
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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)
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Approximation
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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:
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Results from Video-Capture
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Results (contd..)
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Retargeting Motion Key-Shape based
Correspondence also specified between control points etc:.
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Results Video
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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.
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