Presentation of our paper called "A Dynamic Noise Primitive for Coherent Stylization" published at EGSR 2010

1. Pierre BénardJoëlle ThollotGrenoble Universities / INRIA Rhône-AlpesAres Lagae KatholiekeUniversiteit LeuvenREVES - INRIA Sophia-Antipolis Peter VangorpGeorge DrettakisREVES - INRIA Sophia-AntipolisSylvain LefebvreALICE - INRIA Nancy / Loria A Dynamic Noise Primitive for Coherent Stylization

2. Stylization of 3D Animations 3D scene  2D appearance 2

3. Stylization of 3D Animations 3D scene  2D appearance Stylized color regions 2D medium: a pattern Temporal coherence 3 Paint strokes Pencil strokes Paper Watercolor pigments

4. Hand–made animation « Il pleut bergère », Jérémy Depuydt (2005) 4 PoppingTemporal continuity

5. Naïve CG solutions 5 Shower-door effect  Coherent Motion Traditional mapping  Flatness

6. Temporal Coherence Problem Extreme cases  Requirements 6 Flatness Shower-door Popping Coherent motion Temporal continuity Traditional mapping Contradictory requirements: solution  find a compromise

7. 3 goals to ensure at best Additional challenges Flexibility  variety of styles Interactivity  artistic control Evaluation  quality of the trade-off Flatness Coherent motion Temporal continuity 7

8. Previous Work

10. Perspective distortionFlatness [BBT09] Popping Shower-door Coherent motion Temporal continuity Object-space texture mapping[KLK*00,PHWF01,FMS01,BBT09] Traditional mapping 9

13. Popping11 or Flatness [Mei96] Few-primitive methods [Mei96,Dan99,HE04,VBTS07] Screen-space texture mapping[CTP*03,CDH06,BSM*07,BNTS07] Popping Coherent motion Temporal continuity Object-space texture mapping[KLK*00,PHWF01,FMS01,BBT09]

14. Few-Primitive methods 12 Vanderhaeghe et al. EGSR 2007

15. Key Insight Blending a large number of primitives Reduce popping artifacts Individual primitives merge  texture 13

16. Many-Primitive methods [KC05] Flatness Few-primitive methods [Mei96,Dan99,HE04,VBTS07] Screen-space texture mapping[CTP*03,CDH06,BSM*07,BNTS07] Many-primitive methods[KC05,BKTS06] Coherent motion Temporal continuity 14 Object-space texture mapping[KLK*00,PHWF01,FMS01,BBT09]

17. NPR Gabor Noise

18. Procedural noises Sparse convolution [Lewis 84,89] Spot Noise [van Wijk 91] Gabor Noise [LLDD09]  Our trade-off: NPR Gabor Noise 16

19. Gabor Noise [LLDD09] Offers significant spectral control Support anisotropy Is fast to evaluate 17 See “State of the Art in Procedural Noise Functions”, EG 2010 for comparisons with previous work

20. Gabor Noise [LLDD09] Definition Sum of randomly positioned and weighted kernels 18 Gabor kernel noise random positionsand weights

22. Evaluation in 2D screen space19 2D Gabor noise [LLDD09]

24. NPR Gabor Noise Basic principles follow from the goals Flatness Coherent motion 21 Surface Gabor noise [LLDD09] NPR Gabor noise 2D Gabor noise [LLDD09]

29. LOD Mechanism Blending scheme using statistical properties Reduce popping Preserve noise appearance 25

30. Styles

31. Style Design Standard techniques from procedural texturing and modeling [EMPPW02] Threshold Smooth step function X-toon textures [BTM06] Compositing (alpha-blending, overlay) Local control Curvature  noise orientation Shading  noise frequency Interactivefeedback Threshold texture 27

32. Style Design 28

33. Results: 29 isotropic as well asanisotropic patterns

34. 30 local variation according to shading Results:

35. 31 local orientation guided by surface curvature Results:

36. User Study

37. User Study: Motivation Evaluate success of various solutions according to Relative importance of these criteria 33 Flatness Coherent motion Temporal continuity

38. User Study: Setup Methodology 15 naïve subjects, ~ 20-30 minutes Ranking tasks “Rank the images/videos according to … ” 34

39. User Study: Compared methods 35 Local screen-space Global screen-space Object-space Adv D2D DST ours SD TM Extreme cases

40. User Study: Flatness Adv D2D DST ours SD TM Simple stimuli 36 Object-space

41. User Study: Flatness Complex stimuli Adv D2D DST ours SD TM 37

44. Many 3D cues  flatness not perceived38

45. Simple stimuli User Study: Dynamic stimuli 39

46. Complex stimuli User Study: Dynamic stimuli 40

48. Shower-door least coherent

50. Our approach slightly betterthan other image-space methods41

52. Advection and ours produce more changes

54. Others perceived equallyUser Study: Temporal continuity 42

56. Our methods firstimage-space approaches43

58. Conclusions and Future Work 45

60. Flatness hard to see in complex scenes

61. Motion coherence predominant criteriaIntrinsic limitations Hatching  other styles Naïve users  professional artists Objective metric Statistical texture measures [BTS09] Optical flow analysis 46

64. The participants of the study

66. User Study: Flatness “Rank the images according to how flat they appear.” Simple stimuli Complex stimuli less flat more flat less flat more flat 50 50

67. User Study: Coherent motion “Rank the videos according to how coherently the pattern moves with the object.” Simple stimuli Complex stimuli Object-space translate: rotate: zoom: more coherent less coherent less coherent more coherent 51

68. “Rank the videos according to how much the pattern changes over time.” Simple stimuli Complex stimuli User Study: Temporal continuity translate: rotate: zoom: more change less change more change less change 52 52

69. User Study: Pleasantness “Rank the videos according to how pleasant you find them in the context of cartoon animation.” Complex stimuli Object-space less pleasant more pleasant 80 53