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Nonphotorealistic rendering

Nonphotorealistic rendering. Computational Photography, 6.882 Bill Freeman Fredo Durand May 9, 2006 Drawing from: NPR Siggraph 1999 course, Green et al. npr_course_Sig99.pdf. Photorealism. Physically realistic computer graphics rendering

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Nonphotorealistic rendering

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  1. Nonphotorealistic rendering Computational Photography, 6.882 Bill Freeman Fredo Durand May 9, 2006 Drawing from: NPR Siggraph 1999 course, Green et al. npr_course_Sig99.pdf

  2. Photorealism • Physically realistic computer graphics rendering • Images with photographic quality (eg Vermeer, 1632-1675, accused by critics of being cold, inartistic, and displaying ‘spiritual poverty’). http://www.cs.utah.edu/npr/papers/npr_course_Sig99.pdf

  3. Are these images non-photorealistic renderings? http://www.cs.utah.edu/npr/papers/npr_course_Sig99.pdf

  4. Non-photorealistic rendering • Expressive, artistic, painterly, interpretative rendering. • Not aspiring to realism. • Early work: natural media emulation • Pen and ink • Watercolor • Oil on canvas • Attempts to capture the low-level style. • Simulations of technical illustration. http://www.cs.utah.edu/npr/papers/npr_course_Sig99.pdf

  5. NPAR 2002

  6. Comparing photorealism and NPR (Stuart Green) http://www.cs.utah.edu/npr/papers/npr_course_Sig99.pdf

  7. http://www.cs.utah.edu/npr/papers/npr_course_Sig99.pdf

  8. Statistical techniques to simulate expression http://www.cs.utah.edu/npr/papers/npr_course_Sig99.pdf

  9. “Paintings are not solutions to well-posed problems…” http://www.cs.utah.edu/npr/papers/npr_course_Sig99.pdf

  10. Daniel Teece http://pages.cpsc.ucalgary.ca/~mario/npr/projects/sigg03/lec8/hand_1.pdf

  11. Organization of NPR methods • Automated methods • 2-d processing • 3-d processing • Interactive methods • 2-d processing • 3-d processing

  12. Organization of NPR methods • Automated methods • 2-d processing • 3-d processing • Interactive methods • 2-d processing • 3-d processing

  13. 2/2.5 D, no user intervention http://www.cs.utah.edu/npr/papers/npr_course_Sig99.pdf

  14. http://www.mrl.nyu.edu/publications/hertzmann-thesis/hertzmann-thesis-72dpi.pdfhttp://www.mrl.nyu.edu/publications/hertzmann-thesis/hertzmann-thesis-72dpi.pdf

  15. Issues in image style translation • Fitting • Translation

  16. http://people.csail.mit.edu/billf/papers/p33-t_freeman.pdf

  17. http://people.csail.mit.edu/billf/papers/p33-t_freeman.pdf

  18. Input traced line drawing This example will illustrate the tension between fitting and translation http://people.csail.mit.edu/billf/papers/p33-t_freeman.pdf

  19. Input drawing 1-NN fit to input, style 1 Translation to style 2 http://people.csail.mit.edu/billf/papers/p33-t_freeman.pdf

  20. Input drawing 1-NN fit to input, style 1 Translation to style 2 Bad fit, good translation http://people.csail.mit.edu/billf/papers/p33-t_freeman.pdf

  21. Input drawing 5904-NN fit to input, style 1 Translation to style 2. http://people.csail.mit.edu/billf/papers/p33-t_freeman.pdf

  22. Input drawing 5904-NN fit to input, style 1 Translation to style 2. Good fit, bad translation http://people.csail.mit.edu/billf/papers/p33-t_freeman.pdf

  23. Input drawing 6-NN fit to input, style 1 http://people.csail.mit.edu/billf/papers/p33-t_freeman.pdf

  24. Input drawing 6-NN fit to input, style 1 Translation to style 2 Good fit, good translation http://people.csail.mit.edu/billf/papers/p33-t_freeman.pdf

  25. style 1 style 2 style 3 http://people.csail.mit.edu/billf/papers/p33-t_freeman.pdf

  26. http://people.csail.mit.edu/billf/papers/p33-t_freeman.pdf

  27. 6-NN fit to input, style 1 Translation to style 3 http://people.csail.mit.edu/billf/papers/p33-t_freeman.pdf

  28. http://mrl.nyu.edu/projects/image-analogies/

  29. http://mrl.nyu.edu/publications/image-analogies/analogies-72dpi.pdfhttp://mrl.nyu.edu/publications/image-analogies/analogies-72dpi.pdf

  30. Image analogies applications

  31. For painterly style translation, how get the A, A’ image pairs?

  32. http://mrl.nyu.edu/projects/image-analogies/

  33. http://mrl.nyu.edu/projects/image-analogies/

  34. http://mrl.nyu.edu/projects/image-analogies/

  35. Texture Transfer • Take the texture from one object and “paint” it onto another object • This requires separating texture and shape • That’s HARD, but we can cheat • Assume we can capture shape by boundary and rough shading Then, just add another constraint when sampling: similarity to underlying image at that spot http://people.csail.mit.edu/billf/papers/efrosFreeman.pdf

  36. Source correspondence image Target correspondence image Source texture Target image http://people.csail.mit.edu/billf/papers/efrosFreeman.pdf

  37. http://people.csail.mit.edu/billf/papers/efrosFreeman.pdf

  38. A A’

  39. B

  40. B’

  41. B’

  42. I think this one fails

  43. Organization of NPR methods • Automated methods • 2-d processing • 3-d processing • Interactive methods • 2-d processing • 3-d processing

  44. http://www.cs.utah.edu/npr/papers/npr_course_Sig99.pdf

  45. http://www.cs.utah.edu/npr/papers/npr_course_Sig99.pdf

  46. http://www.cs.utah.edu/npr/papers/npr_course_Sig99.pdf

  47. Gooch and Gooch • Concentrate on the material property and shading aspects of technical illustration.

  48. Some characteristics of technical illustrations http://www.cs.utah.edu/npr/papers/npr_course_Sig99.pdf

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