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Real-time Facial Animation Hao Li Mark Pauly ILM EPFL High-End - PowerPoint PPT Presentation

Real-time Facial Animation Hao Li Mark Pauly ILM EPFL High-End 3D Scanning High-End 3D Scanning Low-Cost Passive Scanning (AGI soft) stereo pair Low-Cost Passive Scanning (AGI soft) stereo pair 3D scan Low-Cost Active Scanning +


  1. Real-time Facial Animation Hao Li Mark Pauly ILM EPFL

  2. High-End 3D Scanning

  3. High-End 3D Scanning

  4. Low-Cost Passive Scanning (AGI soft) stereo pair

  5. Low-Cost Passive Scanning (AGI soft) stereo pair 3D scan

  6. Low-Cost Active Scanning + Temporal Upsampling Microsoft Kinect & Kinect Fusion

  7. Rigging & Animation

  8. Rigging & Animation

  9. Typical Facial Animation Workflow in Industry

  10. Typical Facial Animation Workflow in Industry 3D Scanning

  11. Typical Facial Animation Workflow in Industry Modeling + 3D Scanning Fitting

  12. Typical Facial Animation Workflow in Industry Modeling + Light-weight 3D Scanning Fitting Rigging

  13. Typical Facial Animation Workflow in Industry Modeling + Light-weight Motion 3D Scanning Fitting Rigging Capture

  14. Typical Facial Animation Workflow in Industry Modeling + Light-weight Motion Cleanup & 3D Scanning Fitting Rigging Capture Key-Framing

  15. Typical Facial Animation Workflow in Industry Modeling + Light-weight Motion Cleanup & 3D Scanning Fitting Rigging Capture Key-Framing Modeling

  16. Typical Facial Animation Workflow in Industry Modeling + Light-weight Motion Cleanup & 3D Scanning Fitting Rigging Capture Key-Framing Complex Modeling Rigging

  17. Typical Facial Animation Workflow in Industry Modeling + Light-weight Motion Cleanup & 3D Scanning Fitting Rigging Capture Key-Framing Complex Modeling Retargeting Rigging

  18. Typical Facial Animation Workflow in Industry Modeling + Light-weight Motion Cleanup & 3D Scanning Fitting Rigging Capture Key-Framing Complex Modeling Retargeting Rigging

  19. Typical Facial Animation Workflow in Industry Modeling + Light-weight Motion Cleanup & 3D Scanning Fitting Rigging Capture Key-Framing Complex Key-Framing + Modeling Retargeting Rigging Proc.+Sim.

  20. Markerless Facial Capture

  21. 3D range sensor

  22. 3D range sensor

  23. 3D range sensor Motion can be Captured at the Same Resolution as the Geometry

  24. USC ICT Light Stage 5

  25. USC ICT Light Stage 5

  26. Goal

  27. Goal

  28. Template-Based Tracking

  29. Template-Based Tracking

  30. Template-Based Tracking analyze deformation

  31. Template-Based Tracking analyze deformation

  32. Template-Based Tracking transfer deformation

  33. Template-Based Tracking transfer deformation

  34. Template-Based Tracking transfer deformation

  35. Template-Based Tracking transfer deformation

  36. Correspondences Problem

  37. Correspondences Problem

  38. Correspondences Problem

  39. Correspondences Problem

  40. Non-Rigid Registration

  41. Pair of 3D Scans

  42. Pair of 3D Scans source

  43. Pair of 3D Scans target source

  44. Correspondences are Lost

  45. Correspondences are Lost

  46. Correspondences are Lost ?

  47. Overlapping Regions are Lost

  48. Overlapping Regions are Lost overlapping regions

  49. Overlapping Regions are Lost missing data overlapping regions

  50. Overlapping Regions are Lost

  51. Overlapping Regions are Lost

  52. Non-Rigid Registration

  53. Non-Rigid Registration

  54. Three Ingredients

  55. Three Ingredients source

  56. Three Ingredients source target

  57. Three Ingredients source target

  58. Three Ingredients source target detect overlap

  59. Three Ingredients source target detect overlap

  60. Three Ingredients source target detect correspond overlap

  61. Three Ingredients source target detect correspond overlap

  62. Three Ingredients source target detect correspond deform overlap

  63. Three Ingredients registration target detect correspond deform overlap

  64. Challenges detect correspond deform overlap

  65. Challenges detect correspond deform overlap

  66. Challenges detect correspond deform overlap

  67. Challenges deformation detect correspond deform overlap

  68. Challenges detect correspond deform overlap

  69. Challenges detect correspond deform overlap

  70. Challenges ambiguity detect correspond deform overlap

  71. Challenges detect correspond deform overlap

  72. Challenges detect correspond deform overlap

  73. Challenges detect correspond deform overlap

  74. Challenges detect correspond deform overlap

  75. Challenges ? detect correspond deform overlap

  76. Challenges detect correspond deform overlap

  77. Challenges correspond detect deform overlap

  78. Observation correspond detect deform overlap

  79. Observation correspond detect deform overlap

  80. Observation correspond helps detect deform overlap

  81. Observation correspond helps helps detect deform overlap

  82. Observation correspond detect deform overlap

  83. Observation correspond detect deform overlap

  84. Observation correspond detect deform overlap global optimization via local refinement

  85. Iterative Global Optimization correspond detect deform overlap

  86. Iterative Global Optimization correspond detect overlap deform

  87. Iterative Global Optimization correspond detect overlap deform

  88. Iterative Global Optimization correspond detect overlap deform

  89. Iterative Global Optimization correspond detect overlap deform

  90. Iterative Global Optimization correspond detect overlap deform

  91. Iterative Global Optimization closest point detect overlap deform

  92. Iterative Global Optimization closest point detect overlap deform

  93. Iterative Global Optimization closest point pruning deform

  94. Iterative Global Optimization closest point pruning deform

  95. Iterative Global Optimization closest point pruning deform

  96. Iterative Global Optimization closest point pruning deform

  97. Iterative Global Optimization closest point pruning deform

  98. Iterative Global Optimization closest point pruning global optimization

  99. Iterative Global Optimization closest point pruning global optimization

  100. Iterative Global Optimization closest point pruning global optimization converges?

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