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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. Art-Based Rendering of Fur, Grass and Trees.

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Npr today

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


Art based rendering of fur grass and trees

Art-Based Rendering of Fur, Grass and Trees

Michael A. Kowalski et. al.

Presented by Scott Finley

© 2005 University of Wisconsin


Introduction

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


Goals

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


Prior work

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


More prior work

More Prior Work

  • Meier’s particle-based brush strokes showed non-geometric complexity and fixed particle spacing on objects.

© 2005 University of Wisconsin


Software framework

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


Graftal textures

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


Before

Before

© 2005 University of Wisconsin


After

After

© 2005 University of Wisconsin


Difference image algorithm

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


Difference image cont

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


Creating inter frame coherence

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


Subtracting blurred image

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


Graftal sizing

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


Drawing graftals

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


Future work

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


New styles

New Styles

  • Dual layered fur

    • Suggests complex lighting

© 2005 University of Wisconsin


Lecture notes 5274

© 2005 University of Wisconsin


A non photorealistic lighting model for automatic technical illustration

A Non-PhotorealisticLighting Model for Automatic Technical Illustration

Amy Gooch, Bruce Gooch, Peter Shirley, Elaine CohenSIGGRAPH ’98

(presented by)Tom BrunetUniversity of Wisconsin-MadisonCS779


Background

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


Contributions

Contributions

  • Reduction of dynamic range neededto portray shape

  • NPR method for appearance of metal

© 2005 University of Wisconsin


Diffuse shading

Diffuse Shading

© 2005 University of Wisconsin


Highlights and edges

Highlights and Edges

© 2005 University of Wisconsin


Diffuse w edges highlights

Diffuse w/ Edges/Highlights

© 2005 University of Wisconsin


Alter shading model

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


Near constant luminance

Near Constant Luminance

© 2005 University of Wisconsin


Color luminance shift

Color & Luminance Shift

© 2005 University of Wisconsin


Maintains color name

Maintains ‘Color Name’

© 2005 University of Wisconsin


Metal appearance

Metal Appearance

  • Milling creates anisotropic reflection

  • Pick 20 strips of random intensity [0, .5]

  • Linearly interpolate

© 2005 University of Wisconsin


Metallic anisotropic reflection

Metallic, Anisotropic Reflection

© 2005 University of Wisconsin


Approximate in opengl

Approximate in OpenGL

  • Two opposing directional lights:

  • (kwarm - kcool)/2

  • (kcool - kwarm)/2

  • Ambient:(kcool + kwarm)/2

© 2005 University of Wisconsin


Other results questions

Other Results/Questions

© 2005 University of Wisconsin


Lecture notes 5274

© 2005 University of Wisconsin


Computer generated watercolor siggraph 1997

Computer Generated Watercolor(Siggraph 1997)

Cassidy J. Curtis

Sean E. Anderson

Joshua E. Seims

Kurt W. Fleischer

David H. Salesin

© 2005 University of Wisconsin


Watercolor effects

Watercolor Effects

  • Drybrush

  • Edge Darkening

  • Backruns

  • Granulation

  • Flow Effects

  • Glazing

© 2005 University of Wisconsin


Previous work

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


Improvements

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


Algorithm overview

Algorithm Overview

For each time step:

  • MoveWater

    • UpdateVelocities

    • RelaxDivergence

    • FlowOutward

  • MovePigment

  • TransferPigment

  • SimulateCapillaryFlow

© 2005 University of Wisconsin


Algorithm

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


Algorithm1

Algorithm

FlowOutward

  • Remove water from each cell

  • p = p – n * (1 – M’) * M

    MovePigment

  • Pigment distributed to neighboring cells

© 2005 University of Wisconsin


Algorithm2

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


Rendering

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


Rendering limitations

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


Applications

Applications

  • “Interactive” painter

  • Semi-automatic “watercolorization”

  • NPR rendering (“watercolorization” in post)

© 2005 University of Wisconsin


Results

Results

© 2005 University of Wisconsin


Results1

Results

© 2005 University of Wisconsin


Future work1

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


Questions

Questions?

© 2005 University of Wisconsin


Lecture notes 5274

© 2005 University of Wisconsin


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