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  1. NPR Today • “Art-Based Rendering of Fur, Grass and Trees”, Michael A. Kowalski et. al., SIGGRAPH 99 • “A Non-Photorealistic Lighting Model for Automatic Technical Illustration”, Amy Gooch, Bruce Gooch, Peter Shirley and Elaine Cohen, SIGGRAPH ’98 © 2005 University of Wisconsin

  2. Art-Based Rendering of Fur, Grass and Trees Michael A. Kowalski et. al. Presented by Scott Finley © 2005 University of Wisconsin

  3. Introduction • Highly detailed objects containing fur, grass etc. are expensive to render. • This paper attempts to use a well known artistic technique to indicate complexity with simple shapes. © 2005 University of Wisconsin

  4. Goals • Give the designer control over the style. • Simplify modeling by making rendering strategy an aspect of modeling. • Provide interframe coherence for the styles developed. © 2005 University of Wisconsin

  5. Prior Work • Reeves used particle systems to create complex geometry from simple shapes. • Alvy Ray Smith used particles and “graftals” to create his “Cartoon Tree”. • Badler and Glassner generalized the idea of graftals. • This paper uses a modified version of difference images proposed by Salisbury et al. © 2005 University of Wisconsin

  6. More Prior Work • Meier’s particle-based brush strokes showed non-geometric complexity and fixed particle spacing on objects. © 2005 University of Wisconsin

  7. Software Framework • Models are broken into “patches”. Each is rendered by a procedural texture. • Two types of “reference images” are used: • Color reference image • ID reference image • Provides patches with list of pixels • Can be used to find visibility of known point © 2005 University of Wisconsin

  8. Graftal Textures • Place fur, leaves, grass etc. on geometric models. • Need to be drawn in a controlled way in screen space. • Need to stick to models for inter-frame coherence. © 2005 University of Wisconsin

  9. Before © 2005 University of Wisconsin

  10. After © 2005 University of Wisconsin

  11. Difference Image Algorithm • Each patch draws its region in the color reference image. • Darker areas indicate more “desire” for graftals to be placed. • In the examples here we want graftals along the silhouettes. • Render with point light at camera • Can be done explicitly by designer. (Bear’s feet) © 2005 University of Wisconsin

  12. Difference Image Cont. • Graftals are placed according to the desire in the color reference image. • This allows screen space density to be controlled. • Bin all the pixels according to the desire level and start placing graftals on the pixels with the highest desire. © 2005 University of Wisconsin

  13. Creating Inter-frame Coherence • Need to be sure that graftals persist across frames to avoid extreme noise etc. • In first frame place graftals according to DIA. • In further frames attempt place graftals from previous frame. • Place new graftals where needed according to the DIA. © 2005 University of Wisconsin

  14. Subtracting Blurred Image • When a graftal is placed it subtracts a blurred “image” of itself from the reference image. • Graftals are treated as a point for this. The “image” is a Gaussian dot. • Size of the dot is proportional to the screen space area of the graftal. © 2005 University of Wisconsin

  15. Graftal Sizing • Graftals can be set to scale according to perspective, have a constant size, or somewhere between. • Graftal size can be reduced if it tries to draw itself but there isn’t enough desire. © 2005 University of Wisconsin

  16. Drawing Graftals • Fur graftals can be drawn at drawn at different details with triangle strips. • Drawing happens in surface normal plane. • Detail depends on angle to viewer © 2005 University of Wisconsin

  17. Future Work • Reduce flicker/popping as graftals enter and leave. • Use alpha blending • Put graftals on the back of objects • Use several layers of statically placed graftals © 2005 University of Wisconsin

  18. New Styles • Dual layered fur • Suggests complex lighting © 2005 University of Wisconsin

  19. © 2005 University of Wisconsin

  20. A Non-PhotorealisticLighting Model for Automatic Technical Illustration Amy Gooch, Bruce Gooch, Peter Shirley, Elaine CohenSIGGRAPH ’98 (presented by)Tom BrunetUniversity of Wisconsin-MadisonCS779

  21. Background • Various NPR Techniques • Cassidy J. Curtis, Sean E. Anderson, Kurt W. Fleischer, and David H. Salesin. Computer-Generated Watercolor. In SIGGRAPH 97 Conference Proceedings, August 1997. • … • Technical-like • Takafumi Saito and Tokiichiro Takahashi. Comprehensible Rendering of 3D Shapes. In SIGGRAPH 90 Conference Proceedings, August 1990. • Doree Duncan Seligmann and Steven Feiner. Automated Generation of Intent-Based 3D Illustrations. In SIGGRAPH 91 Conference Proceedings, July 1991. • Debra Dooley and Michael F. Cohen. Automatic Illustration of 3D Geometric Models: Surfaces. IEEE Computer Graphics and Applications, 13(2):307-314, 1990. © 2005 University of Wisconsin

  22. Contributions • Reduction of dynamic range neededto portray shape • NPR method for appearance of metal © 2005 University of Wisconsin

  23. Diffuse Shading © 2005 University of Wisconsin

  24. Highlights and Edges © 2005 University of Wisconsin

  25. Diffuse w/ Edges/Highlights © 2005 University of Wisconsin

  26. Alter Shading Model • Want to keep lighting from above • Extend shading across entire sphere: • Finally, mix a cool-warm hue shift with a luminance shift © 2005 University of Wisconsin

  27. Near Constant Luminance © 2005 University of Wisconsin

  28. Color & Luminance Shift © 2005 University of Wisconsin

  29. Maintains ‘Color Name’ © 2005 University of Wisconsin

  30. Metal Appearance • Milling creates anisotropic reflection • Pick 20 strips of random intensity [0, .5] • Linearly interpolate © 2005 University of Wisconsin

  31. Metallic, Anisotropic Reflection © 2005 University of Wisconsin

  32. Approximate in OpenGL • Two opposing directional lights: • (kwarm - kcool)/2 • (kcool - kwarm)/2 • Ambient:(kcool + kwarm)/2 © 2005 University of Wisconsin

  33. Other Results/Questions © 2005 University of Wisconsin

  34. © 2005 University of Wisconsin

  35. Computer Generated Watercolor(Siggraph 1997) Cassidy J. Curtis Sean E. Anderson Joshua E. Seims Kurt W. Fleischer David H. Salesin © 2005 University of Wisconsin

  36. Watercolor Effects • Drybrush • Edge Darkening • Backruns • Granulation • Flow Effects • Glazing © 2005 University of Wisconsin

  37. Previous Work • David Small. Simulating watercolor by modeling diffusion, pigment, and paper fibers. February 1991. • Qinglian Guo and T. L. Kunii. Modeling the diffuse painting of sumie. In T. L. Kunii, editor, IFIP Modeling in Comnputer Graphics. 1991. • Julie Dorsey and Pat Hanrahan. Modeling and rendering of metallic patinas. 1996. © 2005 University of Wisconsin

  38. Improvements • More complex paper model • Better compositing (KM) • Three layer simulation • Shallow-water layer • Pigment disposition layer • Capillary layer • Painting represented as layers of wash (dried watercolor) © 2005 University of Wisconsin

  39. Algorithm Overview For each time step: • MoveWater • UpdateVelocities • RelaxDivergence • FlowOutward • MovePigment • TransferPigment • SimulateCapillaryFlow © 2005 University of Wisconsin

  40. Algorithm UpdateVelocities • Height gradient used to modify velocities • Simulate shallow water flow using Euler Method and standard flow equations • Velocity of pixels outside wet area mask are set to zero RelaxDivergence • Distribute fluid to neighboring cells © 2005 University of Wisconsin

  41. Algorithm FlowOutward • Remove water from each cell • p = p – n * (1 – M’) * M MovePigment • Pigment distributed to neighboring cells © 2005 University of Wisconsin

  42. Algorithm TransferPigment • Pigment is deposited or lifted • Density of pigmentation • Staining power • Granulation SimulateCapillaryFlow • Transfer water from shallow water layer to capillary layer • Water is diffused to neighbors in the capillary layer • Wet area mask updated © 2005 University of Wisconsin

  43. Rendering • Layers combined using Kubelka-Munk method • Interactive pigment creation system • Supports various paint types • Opaque Paints • Transparent Paints • Interference Paints © 2005 University of Wisconsin

  44. Rendering Limitations Kubelka-Munk doesn't account for: • Media of different refractive indices • Uniformly oriented pigment particles • Illumination other than diffuse • Fluorescent paints • Chemical or electrical interaction between different pigments Looks pretty good anyway… © 2005 University of Wisconsin

  45. Applications • “Interactive” painter • Semi-automatic “watercolorization” • NPR rendering (“watercolorization” in post) © 2005 University of Wisconsin

  46. Results © 2005 University of Wisconsin

  47. Results © 2005 University of Wisconsin

  48. Future Work • More effects • Spattering • Hairy brushes • Interaction with pen-and-ink • Fully automatic “watercolorization” • No manual masking • Find optimal palette • Generalization • Backruns and flow effects are really the same • Limit “shower door” effect in "watercolorized" animation. © 2005 University of Wisconsin

  49. Questions? © 2005 University of Wisconsin

  50. © 2005 University of Wisconsin