We present a simple texture synthesis algorithm that is well-suited for a specific class of naturally occurring textures. This class includes quasi-repeating patterns consisting of small objects of familiar but irregular size, such as flower fields, pebbles, forest undergrowth, bushes and tree branches. The algorithm starts from a sample image and generates a new image of arbitrary size the appearance of which is similar to that of the original image. This new image does not change the basic spatial frequencies the original image; instead it creates an image that is a visually similar, and is of a size set by the user. This method is fast and its implementation is straightforward. We extend the algorithm to allow direct user input for interactive control over the texture synthesis process. This allows the user to indicate large-scale properties of the texture appearance using a standard painting-style interface, and to choose among various candidate textures the algorithm can create by performing different number of iterations.

We introduce a general technique for "colorizing" greyscale images by transferring color between a source, color image and a destination, greyscale image. Although the general problem of adding chromatic values to a greyscale image has no exact, objective solution, the current approach attempts to provide a method to help minimize the amount of human labor required for this task. Rather than choosing RGB colors from a palette to color individual components, we transfer the entire color "mood" of the source to the target image by matching luminance and texture information between the images. We choose to transfer only chromatic information and retain the original luminance values of the target image. Further, the procedure is enhanced by allowing the user to match areas of the two images with rectangular swatches. We show that this simple technique can be successfully applied to a variety of images and video, provided that texture and luminance are sufficiently distinct. The images generated demonstrate the potential and utility of our technique in a diverse set of application domains.

We present a BRDF model that combines several advantages of the various empirical models currently in use. In particular, it has intuitive parameters, is anisotropic, conserves energy, is reciprocal, has an appropriate non-Lambertian diffuse term, and is well-suited for use in Monte Carlo renderers.

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We present a mathematical framework for enforcing energy conservation in a BRDF by specifying halfway vector distributions in simple two-dimensional domains. Energy-conserving BRDFs can produce plausible rendered images with accurate reflectance behavior, especially near grazing angles. Using our framework, we create an empirical BRDF that allows easy specification of diffuse, specular, and retroreflective materials. We also present a second BRDF model that is useful for data fitting; although it does not preserve energy, it uses the same halfway vector domain as the first model. We show that this data-fitting BRDF can be used to match measured data extremely well using only a small set of parameters. We believe that this is an improvement over table-based lookups and factored versions of BRDF data.