SCAPE: S hape C ompletion and A nimation PE ople

1. SCAPE: Shape Completion and Animation PEople Stanford University Dragomir Anguelov Praveen Srinivasan Daphne Koller Sebastian Thrun Jim Rodgers UC, Santa Cruz James Davis

2. Shape Completion

3. Animation PEople

4. Overview Non-Linear Optimization Training Data Set Data Acquired Black Box Complete Meshes Human Pose/Shape Parameters

5. Black Box Pose Deformation Model Non-rigid and rigid deformation Shape Deformation Model Variation across different individuals

6. Pose Deformation Model Rigid Transform RL[k] Non-Rigid Transform Qk

7. Y3,k V3,k K-th Tri Y2,k V2,k Y1,k Mesh Reconstruction argminΣkΣj=2,3 || RiL[k]Qikv’jk – (yjk – y1k) ||2 y1, …, ym [Sumner et. al. 2004] Deformation Transfer for Triangle Meshes

8. Learning Parameter Q(R) argminΣkΣj=2,3 || RikQikv’kj – vikj ||2+ {Qi1…QiP} wsΣk1, k2 adjI(Lk1 = Lk2)||Qik1 – Qik2||2 = argmin Reconstruction_Cost + {Qi1…QiP} Smoothness_Cost

9. Parameters of Pose Model Black Box Pose Deformation Model Human Parameters Pose Parameters Q

10. Shape Deformation Model Reconstruction argminΣkΣj=2,3 || RikSikQik(R)v’kj – vikj ||2 {Y1…Ym} V’k,3 V’k,3 V’k,2 V’k,2 Sik

11. Learning Parameter S argminΣkΣj=2,3 || RikSikQikv’kj – vikj ||2+ {Si} wsΣk1, k2 adj||Sik1 – Sik2||2 argmin Reconstruction_Cost + {Si} Smoothness_Cost Si = φU,μ(βi) = Uβi +μ

12. Parameters of Shape Model Estimation of Human Model Black Box Pose Deformation Model Shape Deformation Model Human Parameters Pose Parameters Q Shape Parameters U, μ

13. Estimation of Human Model EH[Y] = argminΣkΣj=2,3 || Rkφ(β)Qkv’jk– (yjk –y1k) ||2 y1, …, ym Q-coefficient Rotation β- mesh coefficient U-EigenVector, μ-mean

14. Shape-Completion / Animation Training Data Set R, β + EH[Y] Q, U, μ EH[Y] + wzΣL||yL - zL||2

15. Limitation • Trained Model (Linear Regression Model) vs. particular pose/shape • Susceptible to local-minimum(?) • Skeleton Based