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Animation PEople
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SCAPE: Shape Completion and Animation PEople
Stanford University Dragomir AnguelovPraveen Srinivasan
Daphne KollerSebastian Thrun
Jim Rodgers
UC, Santa Cruz James Davis
Shape Completion
Animation PEople
Overview
Training Data Set
Black Box
Human Pose/ShapeParameters
Data Acquired Complete Meshes
Non-Linear Optimization
Black Box
Pose Deformation
Model
Non-rigid and rigiddeformation
Shape Deformation
Model
Variation acrossdifferent individuals
Pose Deformation Model
Non-Rigid TransformQk
Rigid TransformRL[k]
Mesh Reconstruction
argminΣkΣj=2,3 || RiL[k]Qi
kv’jk – (yjk – y1k) ||2y1
, …, ym
[Sumner et. al. 2004]Deformation Transferfor Triangle Meshes
Learning Parameter Q(R)argminΣkΣj=2,3 || Ri
kQikv’kj – vi
kj ||2 +{Qi
1…QiP}
wsΣk1, k2 adjI(Lk1 = Lk2)||Qik1 – Qi
k2||2
argmin Reconstruction_CostReconstruction_Cost +{Qi
1…QiP}
Smoothness_CostSmoothness_Cost
=
Parameters of Pose ModelBlack Box
Pose Deformation Model
Human ParametersPose
Parameters Q
Shape Deformation Model
ReconstructionargminΣkΣj=2,3 || Ri
kSikQi
k(R)v’kj – vikj ||2
{Y1…Ym}
V’k,2
V’k,3V’k,2
V’k,3
Sik
Learning Parameter S
argminΣkΣj=2,3 || RikSi
kQikv’kj – vi
kj ||2 +{Si}
wsΣk1, k2 adj||Sik1 – Si
k2||2
argmin Reconstruction_CostReconstruction_Cost +{Si}
Smoothness_CostSmoothness_Cost
Si = φU,μ(βi) = Uβi +μ
Parameters of Shape ModelBlack Box
Pose Deformation Model
ShapeDeformation Model
Pose Parameters Q
ShapeParameters U, μ
Human Parameters
Estimation of Human Model
Estimation of Human ModelEH[Y] =
argminΣkΣj=2,3 || Rkφ(β)Qkv’jk– (yjk –y1k) ||2y1
, …, ym
Q-coefficient
U-EigenVector, μ-mean
Rotationβ- mesh coefficient
Shape-Completion / Animation
Training Data Set
EH[Y]
Q, U, μ
R, β+
EH[Y] + wzΣL||yL - zL||2
Limitation
• Trained Model (Linear Regression Model) vs. particular pose/shape
• Susceptible to local-minimum(?)• Skeleton Based