1. 1 Life Imitates Art Cubist Rendering of Photographs 15-869 Fall 06 Zach Mouri 15-869 Fall 06 Zach Mouri

2. 2 Motivation  Non-Photorealistic Rendering (NPR)  Cubist styling of photographs  More “artistic”  Non-Photorealistic Rendering (NPR)  Cubist styling of photographs  More “artistic”

3. 3 Background  Collomosse, J.P. and P. M. Hall. “Cubist Style Rendering of Photographs.” IEEE Transactions on Visualization and Computer Graphics. Volume 9, Issue 4 (October 2003) pp 443 - 453

4. 4 Cubism  …objects are broken up, analyzed, and re- assembled in an abstracted form…the artist depicts the subject from a multitude of viewpoints to present the piece in a greater context (http://en.wikipedia.org/wiki/Cubism)http://en.wikipedia.org/wiki/Cubism  Pablo Picasso, Georges Braque, Juan Gris  …objects are broken up, analyzed, and re- assembled in an abstracted form…the artist depicts the subject from a multitude of viewpoints to present the piece in a greater context (http://en.wikipedia.org/wiki/Cubism)http://en.wikipedia.org/wiki/Cubism  Pablo Picasso, Georges Braque, Juan Gris

5. 5 Cubist Art The Guitar, 1918 Juan Gris Oil on Canvas The Guitar, 1918 Juan Gris Oil on Canvas

6. 6 Cubist Art Three Musicians, 1921 Pablo Picasso Oil on Canvas Three Musicians, 1921 Pablo Picasso Oil on Canvas

7. 7 Cubist Art Guernica, 1937 Pablo Picasso Oil on Canvas Guernica, 1937 Pablo Picasso Oil on Canvas

8. 8 Process  Choose objects of interest  Geometric distortion  Feature composition  Post-processing (optional)  Choose objects of interest  Geometric distortion  Feature composition  Post-processing (optional)

9. 9 Identifying Features  “Salient” features  Statistically uncommon pixels  Spatially coherent clusters  Manual grouping of high-level features  “Salient” features  Statistically uncommon pixels  Spatially coherent clusters  Manual grouping of high-level features

10. 10 Identifying Features

11. 11 Geometric Distortion  Warp each feature image  Distort curves  angles  Approximated with pincushion warp  Warp each feature image  Distort curves  angles  Approximated with pincushion warp

12. 12 Geometric Distortion

13. 13 Selection and Composition  Select features  Divide features into equivalence classes  Compute set of intervals for each feature  Pick random number; choose feature based on interval  Roughly equivalent to source images (two eyes, one nose, two ears…)  Select features  Divide features into equivalence classes  Compute set of intervals for each feature  Pick random number; choose feature based on interval  Roughly equivalent to source images (two eyes, one nose, two ears…)

14. 14 Selection and Composition EyesNoseEars 0200300550

15. 15 Selection and Composition  Finer segmentation  Determine average area of salient features  Mask non-salient image portions  Produce random, evenly distributed point cloud  Construct Voronoi diagram  Shade non-salient fragments using intensity gradient  Finer segmentation  Determine average area of salient features  Mask non-salient image portions  Produce random, evenly distributed point cloud  Construct Voronoi diagram  Shade non-salient fragments using intensity gradient

16. 16 Selection and Composition

17. 17 Image Rendering  Color quantization  Painter has restricted palette  Introduce false color  Brush strokes  Creates a “painterly” effect  Color quantization  Painter has restricted palette  Introduce false color  Brush strokes  Creates a “painterly” effect

18. 18 Results  Collomosse & Hall

19. 19 Results

20. 20 Results

21. 21 Questions?