1. 03/28/03© 2005 University of Wisconsin NPR Today “Comprehensible Rendering of 3-D Shapes”, Takafumi Saito and Tokiichiro Takahashi, SIGGRAPH 1990 “Painterly Rendering with Curved Brush Strokes of Multiple Sizes”, Aaron Hertzmann, SIGGRAPH 98 “An Algorithm For Automatic Painterly Rendering Based On Local Source Image Approximation”, Michio Shiraishi and Yasushi Yamaguchi, NPAR 2000

2. 03/15/03© 2005 University of Wisconsin Comprehensible Rendering of 3-D Shapes Takafumi Saito and Tokiichiro Takahashi SIGGRAPH 1990

3. 03/15/03© 2005 University of Wisconsin Comprehensible Rendering Focus on conveying information, rather than realism Goal: A range of enhancements to “standard” shaded models –Edge enhancement, contour lines, hatching, …

4. 03/15/03© 2005 University of Wisconsin G-buffers Extend color and depth buffers with additional images –Objects identifiers –Parameters –World coordinates –Normal vectors –… normalsz & uv

5. 03/15/03© 2005 University of Wisconsin Getting G-buffers Almost any algorithm can be modified to produce G-buffers –Ray-tracing – get info from hit points –Hardware – use programmable shaders Common technique now, but done here first PBRT has almost all the required information available

6. 03/15/03© 2005 University of Wisconsin Algorithms Shading Edges/Silhouettes –Find discontinuities in depth Hatching –Trace parameter lines Contours –Trace lines of constant depth

7. 03/15/03© 2005 University of Wisconsin Finding Edges Use edge detection filters on the depth image –Look for places with rapid change in depth –Tuning the filters provides control of extracted edges Then just draw edges, or add back into result

8. 03/15/03© 2005 University of Wisconsin Contours and Hatching Trace lines of constant z to get screen-space contours Or, use world coordinate image to get world-space contours Control spacing to get hatching z=xy

9. 03/15/03© 2005 University of Wisconsin Shading World coordinates and normals enable shading Or, use any procedural shading algorithm Note meta-information is required –e.g. Materials associated with polygon IDs Shading & contours

10. 03/15/03© 2005 University of Wisconsin More Results

11. 03/15/03© 2005 University of Wisconsin More Results

12. 03/15/03© 2005 University of Wisconsin Limitations All results to image precision –May lose contour lines, or they may be hard to track –Use floating point images to help resolve this –Use layered images (multiple surfaces at each pixel) Not all algorithms are amenable to fast hardware implementation

13. 03/15/03© 2005 University of Wisconsin Future Work The ideas from this paper re-emerged in many different algorithms –Generating an image of visible surface IDs was most common (could be done on earliest hardware) Hardware fragment shaders have made much of this redundant –For example, can easily get contour images or false-color

14. 03/15/03© 2005 University of Wisconsin

15. Painterly Rendering with Curved Brush Strokes of Multiple Sizes :Aaron Hertzmann (SIGGRAPH 98 paper) Presented by: Arup Dutta

16. © 2005 University of Wisconsin Painterly rendering Objective: Creating an image with a hand-painted appearance from a photograph Pictorially: From this: Produce this:

17. © 2005 University of Wisconsin Key Concept Painters use varying brush sizes to capture detail:

18. © 2005 University of Wisconsin Algorithm equivalent Procedure: –Build painting one layer at a time, from biggest to smallest radius brushes –At each layer, add detail missing from previous layer Source image= Original photograph Reference image= Gaussian blurred image 3 functions cover this algorithm

19. © 2005 University of Wisconsin function paint(sourceImage, R 1 …R n )

20. © 2005 University of Wisconsin procedure paintLayer(canvas, referenceImage, R) canvas is the image we are updating layer by layer. referenceImage is the Gaussian blurred form of source image Determine grid size for given radius For each pixel of referenceImage calculate area error for grid size. If it is above a certain value, find pixel within grid with largest difference between canvas and referenceImage –Apply makeStroke() algorithm for this point and store the stroke returned. After exiting above ‘for loop’, paint all strokes on the canvas. Gaussian blurred reference image Eucledean difference of r,g,b distances

21. © 2005 University of Wisconsin function makeStroke(x 0,y 0,R,refImage) Generic procedure for placing stroke on canvas Places a circle of radius R at the location x0,y0 Disadvantage: Circles very visible

22. © 2005 University of Wisconsin function makeSplineStroke(x 0,y 0,R,refImage) makeSplineStroke(x 0,y 0,R,refImage): Makes image closer to what a typical painting would look like, by: –Placing long, curved brush strokes, thus eliminating a regular uniform stroke appearance –Placing many small brush strokes in high frequency information region, and placing large brush strokes over areas with little detail.

23. © 2005 University of Wisconsin function makeSplineStroke(x 0,y 0,R,refImage) Brush strokes of constant color Stroke placement normal to image gradient Stroke stops when –color deviates from control point by more than specified threshold OR –predetermined stroke length is reached Stroke points are separated by brush radius distance R. E.g. Image gradient Stroke direction

24. © 2005 University of Wisconsin function makeSplineStroke(x 0,y 0,R,refImage)

25. © 2005 University of Wisconsin Examples Style can be changed by changing parameters of the algorithm such as brush radius, thresholds, adding random jitter etc.

26. © 2005 University of Wisconsin Future Work No one ‘right’ algorithm. Future work should continue to extend strategies to rendering algorithms. Brush strokes could be further developed to convey physical as well as semantic elements Real time rendering where rendering style changes with mood or action

27. © 2005 University of Wisconsin Questions??? Source: UW Madison- CS 559 Spring ’04 Painterly Rendering Assignment sample solution software

28. 03/15/03© 2005 University of Wisconsin

29. 03/15/03 An Algorithm For Automatic Painterly Rendering Based On Local Source Image Approximation Authors Michio ShiraishiYasushi Yamaguchi

30. Goals: Goals:  Automatic Painterly Rendering  Synthesize an image with a handcrafted look from a source image like a photograph.  Intensive use of local region of source image.  High emphasis on color information.

31. Previous Work:  Paul Haeberli developed interactive system where users could place strokes on a canvas.  Barbara Meier introduced 3D model support, painting order is based on depth.  Aaron Hertzmann used brush strokes represented by spline curves. Composite image with several layers of strokes.

32. Process:  Vary stroke size over the canvas. Details are represented by small strokes, flat areas with larger strokes.  Determine location, orientation, width and length of each stroke to approximate the local region of source image.  Paint strokes on order of size.

33. Color Difference Image:  One value set by user is s which is the length and width of the local region.  Grayscale image based on how far local region image is from stroke color.  Intensity is larger the closer the image color is to the stroke color. Stroke color is white in this example. Color image Resulting Gray-scale image

34. Brush Stroke Properties:  Color: The color C in RGB of stroke.  Location: The location of the stroke center (x c, y c ).  Orientation: The angle between the longest axis of stroke and x-axis of canvas.  Size: The size of stroke, length l and width w.

35. How to find these values?:  Image moments are used. Image moments are defined on a gray-scale image.  Image moments of 0 th, 1 st, and 2 nd degree are used.  Use these values to find the length, width, location, and orientation of the stroke. The result is two images with the same image moments. Gray-scale imageResulting Stroke

36. Stroke Painting Process  Scale texture image to fit length and width of stroke.  Rotate scaled image by θ  Translate scaled and rotated image by (x c,y c ).  Paint the stroke with color C by alpha blending.

37. Algorithm:  Stroke Area Estimation:  Calculate M 00 for each pixel with-in local region s.

38. Algorithm:  Sampling Points Generation:  Murray space filling polygon to dither image.  For each pixel, 1/M 00 is summed up until it reaches a certain number.  Then that pixel is a sample pixel.

39. Algorithm:  For each sampling point, calculate the color difference image.  From color difference image, calculate the stroke values.  Paint strokes on white canvas, largest ones first. Larger meaning greater area.

40. Results: 2500 Strokes5000 Strokes7500 Strokes10000 Strokes Original s=25, 11048 Strokess=15, 9673 Strokes

41. Future Work:  Make the algorithm calculate the s value of the local region size.  Calculate the minimum number of strokes to cover entire canvas.