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Automatic Modeling of 3D Human Face

Automatic Modeling of 3D Human Face. Supervisors :. Marco Andolfi. I. Ragnemalm Institute of Technology of Linkoping. M. Schaerf “La Sapienza ” University of Rome. Co-Supervisor :. M. Fratarcangeli “La Sapienza ” University of Rome. The problem : Face modeling.

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Automatic Modeling of 3D Human Face

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  1. Automatic Modeling of 3D Human Face Supervisors: Marco Andolfi I. Ragnemalm Institute of Technology of Linkoping M. Schaerf “La Sapienza” University of Rome Co-Supervisor: M. Fratarcangeli “La Sapienza” University of Rome

  2. The problem: Facemodeling Modeling System Input Output Our goal

  3. Our system:Input - Output - Features Modeling System Input Output 1 - FiveOrthogonalPhotos 3D colored model of the subject’s head in photo (no hair) 2 – A “Generic” Mesh 3 – A Landmark set points No particularinstrument

  4. The Input(1) - FiveorthogonalPhotos

  5. The Input:(2) – A “generic” Mesh Withoutexpression Without Gender Features Without Race Features

  6. The Input:(3) – The landmark set ofpoints (almost) MPEG-4landmarkpoints

  7. Inside the system Modeling System Coloring Sub-System Morphing Sub-System

  8. Morphing sub system

  9. Morphing sub system Morphing Sub-System MeshFeature Extraction Photo Feature Extraction Camera Error Correction 2D – 3D Conversion MorphingExecution

  10. Meshfeatureextraction Manuallypicking Preprocessingoperation

  11. Morphing sub system Morphing Sub-System MeshFeature Extraction Photo Feature Extraction Camera Error Correction 2D – 3D Conversion MorphingExecution

  12. Photo featureextraction Manuallypicking Noisy Data

  13. Morphing sub system Morphing Sub-System MeshFeature Extraction Photo Feature Extraction Camera Error Correction 2D – 3D Conversion MorphingExecution

  14. Camera Errors: thecorrectedones Differentdistance Camera-subject Scaling

  15. Camera Errors: thecorrectedones Differentdistance Camera-subject Scaling Camera orientation Translation

  16. Camera Errors: thecorrectedones Differentdistance Camera-subject Scaling Camera orientation Translation Wrong Head Position: Zaxis Rotation

  17. Camera Errors: thenegligibleones Wrong Head Position: Xaxis Rotation Angle?

  18. Camera Errors: thenegligibleones Wrong Head Position: Xaxis Rotation Angle? Wrong Head Position: Yaxis Rotation No info

  19. Camera Errors: thenegligibleones Wrong Head Position: Xaxis Rotation Angle? Wrong Head Position: Yaxis Rotation No info Prospective error Negligible

  20. Camera Errors: Summary Corrected Ignored Differentdistance Camera-subject Wrong Head Position: Xaxis Rotation Angle? Scaling Camera orientation Wrong Head Position: Yaxis Rotation No info Translation Wrong Head Position: Zaxis Prospective error Rotation Negligible

  21. Getreferencepoint: Why? Rotation Scaling

  22. Getreferencepoint:Why? Translation

  23. Camera Errors: Getreferencepoint

  24. Camera Errors: Correction

  25. Getscalingfactor: example on Y Noisy Data Bad Result Fy Ly Fy ScalingFactor = ----------- Ly

  26. Getscalingfactor: example on Y Fy1 Fy2 Ly1 Ly2 Fy1 + Fy2 + ... + FyN ScalingFactor = --------------------------------------- Ly1 + Ly2 + ... + LyN

  27. Morphing sub system Morphing Sub-System MeshFeature Extraction Photo Feature Extraction Camera Error Correction 2D – 3D Conversion MorphingExecution

  28. 2D to 3D convertion Severalvaluesforeach coordinate WeightedAverage More importanceto front photo

  29. Morphing sub system Morphing Sub-System MeshFeature Extraction Photo Feature Extraction Camera Error Correction 2D – 3D Conversion MorphingExecution

  30. Single stepmorphing Noisy Data Interpolation RBF Morphingfunction Lowstiffnessparameter (asinterpolation) Bad Result

  31. Single stepmorphing: whynot...

  32. Twodifferentlevel of quality High levelqualitypoints Lowlevelqualitypoints

  33. Doublestepmorphing Low and Highlevelqualitypoints RBF Morphingfunction High stiffnessparameter (asapproximation) First step c RBF Morphingfunction Lowstiffnessparameter (asinterpolation) Secondstep High levelqualitypoints

  34. Inside the system Modeling System Coloring Sub-System Morphing Sub-System

  35. Coloring sub system

  36. Coloring sub-system Coloring Sub-System Texture coordinate computing Weight computing Bottomtexture generation Texturemapping Remove ghost effect Solve Overlapartifact

  37. Texture coordinate computing Whatabout the cameracorrection error? Not a simpleorthogonalprojection

  38. Texture coordinate computing

  39. Coloring sub-system Coloring Sub-System Texture coordinate computing Weight computing Bottomtexture generation Texturemapping Remove ghost effect Solve Occlusion Problem

  40. Weight computing γ β α Avoidingoverlapping Why? The weightastransparencylevel NT Weightforvertexrespect front photo NP Proportionaltoα NR NL WF = NF∙ NP Set NULL negative weights NF WR = NR∙ NP < 0 → WR = 0

  41. Coloring sub-system Coloring Sub-System Texture coordinate computing Weight computing Bottomtexture generation Texturemapping Remove ghost effect Solve Overlappartifact

  42. Bottomtexture generation AIM: obtain a texturecontainigonlyskin Little squareonlyskin Repetition of the little square Final bottomtexture No mole, Noscar Bad quality? (toomanysquares...)

  43. Coloring sub-system Coloring Sub-System Texture coordinate computing Weight computing Bottomtexture generation Texturemapping Remove ghost effect Solve Overlappartifact

  44. TextureMapping The weightastransparencylevel

  45. Coloring sub-system Coloring Sub-System Texture coordinate computing Weight computing Bottomtexture generation Texturemapping Remove ghost effect Solve Overlappartifact

  46. Ghost effect

  47. Coloring sub-system Coloring Sub-System Texture coordinate computing Weight computing Bottomtexture generation Texturemapping Remove ghost effect Solve Overlappartifact

  48. Solvingocclusionproblem

  49. Solvingocclusionproblem Detectvertexbetweeninnereyepoints Givingweightto side photos Removingweightto side photos

  50. Let’s see some examples

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