04/01/05© 2005 University of Wisconsin 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

04/01/05© 2005 University of Wisconsin Art-Based Rendering of Fur, Grass and Trees Michael A. Kowalski et. al. Presented by Scott Finley

© 2005 University of Wisconsin 04/01/05 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 04/01/05 Goals 1. Give the designer control over the style. 2. Simplify modeling by making rendering strategy an aspect of modeling. 3. Provide interframe coherence for the styles developed.

© 2005 University of Wisconsin 04/01/05 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 04/01/05 More Prior Work Meier’s particle-based brush strokes showed non-geometric complexity and fixed particle spacing on objects.

© 2005 University of Wisconsin 04/01/05 Software Framework Models are broken into “patches”. Each is rendered by a procedural texture. Two types of “reference images” are used: 1. Color reference image 2. ID reference image  Provides patches with list of pixels  Can be used to find visibility of known point

© 2005 University of Wisconsin 04/01/05 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 04/01/05 Before

© 2005 University of Wisconsin 04/01/05 After

© 2005 University of Wisconsin 04/01/05 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 04/01/05 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 04/01/05 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 04/01/05 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 04/01/05 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 04/01/05 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 04/01/05 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 04/01/05 New Styles Dual layered fur  Suggests complex lighting

04/01/05© 2005 University of Wisconsin

A Non-Photorealistic Lighting Model for Automatic Technical Illustration (presented by) Tom Brunet University of Wisconsin-Madison CS779 Amy Gooch, Bruce Gooch, Peter Shirley, Elaine Cohen SIGGRAPH ’98

04/01/05© 2005 University of Wisconsin 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 –… Technical-like –Takafumi Saito and Tokiichiro Takahashi. Comprehensible Rendering of 3D Shapes. In SIGGRAPH 90 Conference Proceedings, August –Doree Duncan Seligmann and Steven Feiner. Automated Generation of Intent-Based 3D Illustrations. In SIGGRAPH 91 Conference Proceedings, July –Debra Dooley and Michael F. Cohen. Automatic Illustration of 3D Geometric Models: Surfaces. IEEE Computer Graphics and Applications, 13(2): , 1990.

04/01/05© 2005 University of Wisconsin Contributions Reduction of dynamic range needed to portray shape NPR method for appearance of metal

04/01/05© 2005 University of Wisconsin Diffuse Shading k a : ambient illumination k d : diffuse reflectance

04/01/05© 2005 University of Wisconsin Highlights and Edges

04/01/05© 2005 University of Wisconsin Diffuse w/ Edges/Highlights k a : ambient illumination k d : diffuse reflectance

04/01/05© 2005 University of Wisconsin 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

04/01/05© 2005 University of Wisconsin Near Constant Luminance

04/01/05© 2005 University of Wisconsin Color & Luminance Shift

04/01/05© 2005 University of Wisconsin Maintains ‘Color Name’

04/01/05© 2005 University of Wisconsin Metal Appearance Milling creates anisotropic reflection Pick 20 strips of random intensity [0,.5] Linearly interpolate

04/01/05© 2005 University of Wisconsin Metallic, Anisotropic Reflection

04/01/05© 2005 University of Wisconsin Approximate in OpenGL Two opposing directional lights: (k warm - k cool )/2 (k cool - k warm )/2 Ambient: (k cool + k warm )/2

04/01/05© 2005 University of Wisconsin Other Results/Questions

04/01/05© 2005 University of Wisconsin

04/01/05© 2005 University of Wisconsin Computer Generated Watercolor (Siggraph 1997) Cassidy J. Curtis Sean E. Anderson Joshua E. Seims Kurt W. Fleischer David H. Salesin

04/01/05© 2005 University of Wisconsin Watercolor Effects Drybrush Edge Darkening Backruns Granulation Flow Effects Glazing

04/01/05© 2005 University of Wisconsin Previous Work David Small. Simulating watercolor by modeling diffusion, pigment, and paper fibers. February Qinglian Guo and T. L. Kunii. Modeling the diffuse painting of sumie. In T. L. Kunii, editor, IFIP Modeling in Comnputer Graphics Julie Dorsey and Pat Hanrahan. Modeling and rendering of metallic patinas

04/01/05© 2005 University of Wisconsin 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)

04/01/05© 2005 University of Wisconsin Algorithm Overview For each time step: MoveWater –UpdateVelocities –RelaxDivergence –FlowOutward MovePigment TransferPigment SimulateCapillaryFlow

04/01/05© 2005 University of Wisconsin 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

04/01/05© 2005 University of Wisconsin Algorithm FlowOutward Remove water from each cell p = p – n * (1 – M’) * M MovePigment Pigment distributed to neighboring cells

04/01/05© 2005 University of Wisconsin 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

04/01/05© 2005 University of Wisconsin Rendering Layers combined using Kubelka-Munk method Interactive pigment creation system Supports various paint types –Opaque Paints –Transparent Paints –Interference Paints

04/01/05© 2005 University of Wisconsin 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…

04/01/05© 2005 University of Wisconsin Applications “Interactive” painter Semi-automatic “watercolorization” NPR rendering (“watercolorization” in post)

04/01/05© 2005 University of Wisconsin Results

04/01/05© 2005 University of Wisconsin Results

04/01/05© 2005 University of Wisconsin 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.

04/01/05© 2005 University of Wisconsin Questions?

04/01/05© 2005 University of Wisconsin