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Comp 767: Advanced Topics in Graphics Computer-Generated Watercolor Cassidy J. CurtisSean E. Anderson Kurt W. FleischerDavid H. Salesin Irwin Chiu Hau.

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Presentation on theme: "Comp 767: Advanced Topics in Graphics Computer-Generated Watercolor Cassidy J. CurtisSean E. Anderson Kurt W. FleischerDavid H. Salesin Irwin Chiu Hau."— Presentation transcript:

1 Comp 767: Advanced Topics in Graphics Computer-Generated Watercolor Cassidy J. CurtisSean E. Anderson Kurt W. FleischerDavid H. Salesin Irwin Chiu Hau Computer Science McGill University Winter 2004

2 Overview Introduction Introduction Properties of watercolor Properties of watercolor Computer-generated watercolor Computer-generated watercolor Applications Applications Future work Future work Conclusion Conclusion

3 Introduction What is watercolor painting? What is watercolor painting? Computer generated watercolor as a non-photorealistic rendering Computer generated watercolor as a non-photorealistic rendering

4 Overview Introduction Introduction Properties of watercolor Properties of watercolor Computer-generated watercolor Computer-generated watercolor Applications Applications Future work Future work Conclusion Conclusion

5 Properties of Watercolor Watercolor materials Watercolor materials Watercolor paper Watercolor paper Pigment Pigment Binder Binder Surfactant Surfactant Watercolor effects Watercolor effects

6 Watercolor Paper Typically not made of wood pulp Typically not made of wood pulp But from linen or cotton rags pounded into small fibers  Extremely absorbent to liquids Filled with sizing usually made of cellulose Filled with sizing usually made of cellulose  Slows down the rate of water absorption and diffusion Source: misterart.com

7 Pigment A pigment is a solid material in the form of small, separate particles (ranging from 0.05 to 0.5 microns) Pigments vary in density

8 Binder and Surfactant Binder Adsorption Adsorption Enables the pigment to adhere to the paper Surfactant Allows water to soak into sized paper Binder Source: Jerry’s ARTARAMA

9 Properties of watercolor Watercolor materials Watercolor materials Watercolor effects Watercolor effects Dry-brush effects Dry-brush effects Edge darkening Edge darkening Intentional backruns Intentional backruns Granulation and Separation Granulation and Separation Flow Patterns Flow Patterns Glazing Glazing

10 Dry-brush Effects Techniques Techniques Dry brush that is almost dried Applied at a proper angle Effects Effects Irregular gaps Ragged edges Source: Computer Generated Watercolor

11 Edge Darkening Techniques Techniques Wet-on-dry brushstroke Effect Effect Darken edges Source: Computer Generated Watercolor

12 Intentional Backruns Occurs when Occurs when A puddle of water spread back into a damp region of paint A wash brush dries unevenly The water tends to push pigment along as it spreads Effect Effect Complex branching shapes Severely darkened edges Source: Computer Generated Watercolor

13 Granulation and Separation of Pigments Granulation of pigments Granulation of pigments Yields a kind of grainy textures Varies from pigment to pigment Strongest when paper is very wet Separation of pigment Separation of pigment Refers to splitting of colors Occurs when denser pigments settle earlier than lighter ones Source: Computer Generated Watercolor

14 Flow Patterns In wet-in-wet painting In wet-in-wet painting wet surface allows the brushstrokes to spread freely Effects Effects Soft, feathery shapes Source: Computer Generated Watercolor

15 Glazing Techniques Adding very thin, pale layers, or washes, of watercolor, one over another Different pigments are not mixed physically, but optically Effects Effects luminous glowing from within Source: Computer Generated Watercolor

16 Overview Introduction Introduction Properties of watercolor Properties of watercolor Computer-generated watercolor Computer-generated watercolor Applications Applications Future work Future work Conclusion Conclusion

17 Computer-Generated Watercolor Simulated watercolor effects Source: Computer Generated Watercolor Real watercolor effects

18 Implementation Paper generation Paper generation Data structure Data structure Fluid simulation Fluid simulation Optical compositing Optical compositing

19 Paper Generation Use a simple model Use a simple model Paper texture is modeled as a height field h and a fluid capacity field c h is pseudo-randomly generated, 0 < h < 1 c = c = h * (c max – c min ) + c min Example paper texturesSource: Computer Generated Watercolor

20 Data Structure A complete painting consists of A complete painting consists of Ordered set of washes over a sheet of paper Ordered set of washes over a sheet of paper Each wash may contain Each wash may contain Various pigments in varying quantities over different parts of the image Various pigments in varying quantities over different parts of the image We store these quantities in We store these quantities in A data structure called a ‘glaze’ A data structure called a ‘glaze’

21 Glaze Each glaze is created Each glaze is created by running a fluid simulation by running a fluid simulation Inputs: Inputs: Properties of pigments, paper, watercolor medium Properties of pigments, paper, watercolor medium Wet-area mask Wet-area mask Once the glazes are computed Once the glazes are computed They are optically composited They are optically composited using the Kubelka-Munk color model

22 The Fluid Simulation Each wash Each wash simulated using a three-layer model Source: Computer Generated Watercolor

23 The Fluid Simulation Main loop proc MainLoop for each time step do: MoveWater MovePigment TransferPigment SimulateCapillaryFlow end for end proc

24 The Fluid Simulation Cellular Automaton Cellular Automaton Definition from Mathworld.com Definition from Mathworld.com A cellular automaton is a collection of "colored" cells on a grid of specified shape that evolves through a number of discrete time steps according to a set of rules based on the states of neighboring cells A cellular automaton is a collection of "colored" cells on a grid of specified shape that evolves through a number of discrete time steps according to a set of rules based on the states of neighboring cells Game of Life Source: Mathworld.com

25 Move Water proc MoveWater(M, u, v, p): UpdateVelocities(M, u, v, p) RelaxDivergence(M, u, v, p) FlowOutward(M, p) end proc edge darkening M : wet-area mask u, v : velocity p : water pressure

26 Move Pigment Pigments move within the shallow-water layer as specified by the velocity field u, v Pigment from each cell are distributed to its neighbors at the rate of fluid movement out of the cell

27 Transfer Pigment Pigment adsorption and desorption Pigment adsorption and desorption proc TransferPigment(g 1,...,g n,d 1,...,d n ): for each pigment k do for all cells (i, j) do … g, d : pigment concentrations Source: Computer Generated Watercolor

28 Simulate Capillary Flow Diffusing water through the capillary layer proc SimulateCapillaryFlow(s, M ): for each pigment k do for all cells (i, j) do … backruns s : water saturation of the paper dry-brush effects Source: Computer Generated Watercolor

29 The Fluid Simulation Main loop proc MainLoop(M, u, v, p, g 1, …, g n, d 1, …, d n, s ): for each time step do: MoveWater(M, u, v, p) MovePigment(M, u, v, g 1, …, g n ) TransferPigment(g 1, …, g n, d 1, …, d n ) SimulateCapillaryFlow(M, s) end for end proc initial wet-area mask initial velocity initial water pressure initial pigment concentrations initial water saturation of the paper

30 Optical compositing Rendering the pigmented layers Use the Use the Kubelka-Munk (KM) model to perform the optical compositing of glazing layers Source: Computer Generated Watercolor

31 Kubelka-Munk (KM) Model Comes from KM Theory Comes from KM Theory Tells us how to Tells us how to specify the optical properties of pigments specify the optical properties of pigments optically composite pigments optically composite pigments optically composite layers optically composite layers

32 Overview Introduction Introduction Properties of watercolor Properties of watercolor Computer-generated watercolor Computer-generated watercolor Applications Applications Future work Future work Conclusion Conclusion

33 Applications Interactive painting with watercolors Interactive painting with watercolors Automatic image “watercolorization” Automatic image “watercolorization” Non-photorealistic rendering of 3D models Non-photorealistic rendering of 3D models

34 Interactive Painting User creates User creates Glazes Glazes User adjusts User adjusts Brush sizes Brush sizes Pigments Pigments Wet-mask area Wet-mask area Physical parameters Physical parameters Source: Computer Generated Watercolor

35 Automatic image “watercolorization” “Automatically” convert a color image into “Automatically” convert a color image into a watercolor illustration Is done in two steps Is done in two steps Color separation Color separation Brushstroke planning Brushstroke planning

36 Color Separation Color Separation Process Source: Computer Generated Watercolor

37 Brushstroke Planning Too much pigment Thins them by adding water Lack of pigment Add a pigmented wash Brushstroke PlanningSource: Computer Generated Watercolor Painter control the concentration and the flow of pigment in a wash Painter control the concentration and the flow of pigment in a wash

38 Automatic image “watercolorization” An automatic watercolorization Source: Computer Generated Watercolor Original image

39 Steps for Rendering Source: Computer Generated Watercolor

40 Non-photorealistic rendering of 3D models Given a 3D geometric scene, we Given a 3D geometric scene, we automatically generate mattes isolating each object These mattes are used as input to the watercolorization process The user specifies the pigment choices and brushstroke planning

41 Non-Photorealistic Animation Several frames from a non-photorealistic animation of moving clouds Source: Computer Generated Watercolor 3D Scene Detail of one frame

42 Overview Introduction Introduction Properties of watercolor Properties of watercolor Computer-generated watercolor Computer-generated watercolor Applications Applications Future work Future work Conclusion Conclusion

43 Future Work Other effects Other effects Automatic rendering Automatic rendering Generalization Generalization Animation Issues Animation Issues

44 Overview Introduction Introduction Properties of watercolor Properties of watercolor Computer-generated watercolor Computer-generated watercolor Applications Applications Future work Future work Conclusion Conclusion

45 Conclusion That’s all about That’s all about Computer Generated Watercolor Questions ??? Questions ??? Discussions ??? Discussions ???

46 References Cassidy J. Curtis, Sean E. Anderson, Kurt W. Fleischer and David H. Salesin. Computer-Generated Watercolor Cassidy J. Curtis, Sean E. Anderson, Kurt W. Fleischer and David H. Salesin. Computer-Generated Watercolor Images Images


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