This thesis presents a general method and guidelines for compositing digital characters into traditional artwork by matching a character to the perspective, lighting, style, and complexity of the particular work of art. The primary goal of this integration is to make the resulting image believable, but not necessarily to create an exact match. As a result, the approach used here is not limited to a single rendering style or medium, but can be used to create a very close match for almost any artistic image. To develop and test this method and set of guidelines I created composites using a variety of styles and mediums.

The purpose of this thesis is to develop a texture-based painterly shader that would render computer generated objects or scenes with strokes that are visually similar to paint media like watercolor, oil paint or dry media such as crayons, chalk, et cetera. This method would need an input scene in the form of three dimensional polygonal or NURBS meshes. While the structure of the meshes and the lighting in the scene would both play a crucial role in the final appearance of the scene, the painterly look will be imparted through a shader. This method, therefore, is essentially a rendering technique. Several modifiable parameters in the shader gives the user artistic freedom while overall introducing some amount of automation in the painterly rendering process.

The purpose of this thesis is to develop a texture-based painterly shader that would render computer generated objects or scenes with strokes that are visually similar to paint media like watercolor, oil paint or dry media such as crayons, chalk, et cetera. This method would need an input scene in the form of three dimensional polygonal or NURBS meshes. While the structure of the meshes and the lighting in the scene would both play a crucial role in the final appearance of the scene, the painterly look will be imparted through a shader. This method, therefore, is essentially a rendering technique. Several modifiable parameters in the shader gives the user artistic freedom while overall introducing some amount of automation in the painterly rendering process.

There currently exist several techniques for selecting and combining images from a digital image library into a single image so that the result meets certain prespecified visual criteria. Image mosaic methods, first explored by Connors and Trivedi[18], arrange library images according to some tiling arrangement, often a regular grid, so that the combination of images, when viewed as a whole, resembles some input target image. Other techniques, such as Autocollage of Rother et al.[78], seek only to combine images in an interesting and visually pleasing manner, according to certain composition principles, without attempting to approximate any target image. Each of these techniques provide a myriad of creative options for artists who wish to combine several levels of meaning into a single image or who wish to exploit the meaning and symbolism contained in each of a large set of images through an efficient and easy process. We first examine the most notable and successful of these methods, and summarize the advantages and limitations of each. We then formulate a set of goals for an image collage system that combines the advantages of these methods while addressing and mitigating the drawbacks. Particularly, we propose a system for creating photocollages that approximate a target image as an aggregation of smaller images, chosen from a large library, so that interesting visual correspondences between images are exploited. In this way, we allow users to create collages in which multiple layers of meaning are encoded, with meaningful visual links between each layer. In service of this goal, we ensure that the images used are as large as possible and are combined in such a way that boundaries between images are not immediately apparent, as in Autocollage. This has required us to apply a multiscale approach to searching and comparing images from a large database, which achieves both speed and accuracy. We also propose a new framework for color post-processing, and propose novel techniques for decomposing images according to object and texture information.

Dendritic or branching structures are commonly seen in natural phenomena such as lightning, cracking and vegetal growth. They are also often used for artistic or decorative purposes, ranging from ornamentation to decorative ceramics. Existing procedural methods for modeling these structures remain very limited in terms of control and flexibility. As a result, these objects tend to be modeled individually, which is a painstaking and costly process.<p>We present a new procedural method for modeling dendritic structures based on a path planning approach. Our method includes the implementation of a partial non-scalar distance metric that gives us effective and flexible control handles over the evolving dendritic structure. These control handles are demonstrated by guiding the growth of dendritic structures using input images, allowing us to create a form of stylistic dendritic halftoning and to embed hidden images in dendritic trees to create pareidolia effects. These applications demonstrate the vast diversity of structures that can easily be modeled by our process a flexibility that existing methods definitely lack. We also demonstrate the application of the partial non-scalar distance metric to the context of texture synthesis from example, and show how it holds promise for many other contexts.

Dendritic or branching structures are commonly seen in natural phenomena such as lightning, cracking and vegetal growth. They are also often used for artistic or decorative purposes, ranging from ornamentation to decorative ceramics. Existing procedural methods for modeling these structures remain very limited in terms of control and flexibility. As a result, these objects tend to be modeled individually, which is a painstaking and costly process.<p>We present a new procedural method for modeling dendritic structures based on a path planning approach. Our method includes the implementation of a partial non-scalar distance metric that gives us effective and flexible control handles over the evolving dendritic structure. These control handles are demonstrated by guiding the growth of dendritic structures using input images, allowing us to create a form of stylistic dendritic halftoning and to embed hidden images in dendritic trees to create pareidolia effects. These applications demonstrate the vast diversity of structures that can easily be modeled by our process a flexibility that existing methods definitely lack. We also demonstrate the application of the partial non-scalar distance metric to the context of texture synthesis from example, and show how it holds promise for many other contexts.

There currently exist several techniques for selecting and combining images from a digital image library into a single image so that the result meets certain prespecified visual criteria. Image mosaic methods, first explored by Connors and Trivedi[18], arrange library images according to some tiling arrangement, often a regular grid, so that the combination of images, when viewed as a whole, resembles some input target image. Other techniques, such as Autocollage of Rother et al.[78], seek only to combine images in an interesting and visually pleasing manner, according to certain composition principles, without attempting to approximate any target image. Each of these techniques provide a myriad of creative options for artists who wish to combine several levels of meaning into a single image or who wish to exploit the meaning and symbolism contained in each of a large set of images through an efficient and easy process. We first examine the most notable and successful of these methods, and summarize the advantages and limitations of each. We then formulate a set of goals for an image collage system that combines the advantages of these methods while addressing and mitigating the drawbacks. Particularly, we propose a system for creating photocollages that approximate a target image as an aggregation of smaller images, chosen from a large library, so that interesting visual correspondences between images are exploited. In this way, we allow users to create collages in which multiple layers of meaning are encoded, with meaningful visual links between each layer. In service of this goal, we ensure that the images used are as large as possible and are combined in such a way that boundaries between images are not immediately apparent, as in Autocollage. This has required us to apply a multiscale approach to searching and comparing images from a large database, which achieves both speed and accuracy. We also propose a new framework for color post-processing, and propose novel techniques for decomposing images according to object and texture information.

In this thesis, we present a method for computing an artistic representation of ocean waves using Bezier curves. Wave forms are loosely based on procedural wave models and are designed to emulate those found in both art and nature. The wave forms are generated using a slice method which is user defined by structured input, thus providing the artist with full control over crest shape and placement. Wave propagation is obtained by interpolating between defined crest shapes and positions. We also present a method for computing a stylized representation of breaking crests in shallow water. Artists may use our model to create many interesting wave forms, including basic sinusoidal waves and waves with breaking crests that have a rotation that is cyclical in time. The major drawbacks to our solution are that data entry can be tedious and it can be difficult to produce waves that animate with a natural appearance.

In this dissertation I examine how research in non-photorealistic rendering, human perception, and game-based learning can be combined to produce illustrative simulations of different visual systems that effectively convey information about vision to unprimed observers. The Visual Differences Simulation (VDS) methodology and framework that I propose is capable of producing simulations of animal visual systems based on how they relate to human vision, and can represent differences in color vision, hyperspectral sensitivity, visual acuity, light sensitivity, field of view, motion sensitivity, and eye construction. The simulations produced by the VDS framework run in real time, allowing users to explore computer-generated environments from `behind the eyes' of an animal in an interactive and immersive manner. I also examine how cognitive principles and game-based learning can be leveraged to demonstrate and enhance the educational impact of the simulations produced by the VDS framework. Two case studies are presented, where simulations of the cat and the bee visual systems are used as the basis to design educational games, and are evaluated to show that embedding the simulations in educational games is an effective and engaging way to convey information about vision to unprimed observers. / Graduate

Much research has been done in creating non-photorealistic renderings of objects that mimic the look of hand-made drawings by traditional artists. This thesis extends work in this area by presenting an NPR hatching method that can be applied to 3D animated films to help them feel more hand drawn. In contrast to most other NPR methods, this method preserves the 3D lighting and effects of the film that make it interesting to watch. This process includes a procedural algorithm to create a hatching pattern that can be easily integrated into any film's pipeline that uses Renderman. In addition, we create a set of controls to adjust the hatching that are easy to use and allow our style to be applied to many different objects in many shots of a film in an efficient manner. To show the success of our method, we will discuss the implementation and results of applying it to an actual 3D animated short film.