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