One of the most difficult tasks in the computer graphics field is the simulation of human behavior. Some researchers have already tried to generate human behavior in a virtual space by introducing psychology and/or by considering collision avoidance methods. Many previous works, however, have a control center that knows everything about individual position and going direction and so forth, and informs some of it to each person. In addition, individual and crowd behavior are different and treated separately. This paper proposes a virtual human model who has his/her own eye with which each person can obtain the information necessary for their actions by themselves. In addition, we have tried to simulate crowd behavior by applying the individual human model for many people without introducing particle or flocking systems.

Physics based simulation is one of the most important issues in computer graphics since it validates the simulation model and improves the quality of the images generated according to the simulation results. The visualization of natural phenomena is a challenging issue among them and the simulation on fluid is the most difficult one because the shape of fluid changes more dynamically than solid and the boundary is clearer than gas. In this paper, we describe the model and the method to visualize a waterfall by using a hybrid method of Lagrangian (particle) and Eulerian (grid-based) approaches. The simulation starts from the lip of a waterfall and ends at the basin, and it also treats the stream of a waterfall, the spray splashing from the stream, and the spray cloud being sent up from the stream and/or the basin. As the result of the simulation, we have succeeded in visualizing waterfall behavior, which includes the water stream, the splashing spray and the spray cloud.

Color transformation is the most effective method to improve the mood of an image, because color has a large influence in forming the mood. However, conventional color transformation tools have a tradeoff between the quality of the resultant image and the amount of manual operation. To achieve a more detailed and natural result with less labor, we previously suggested a method that performs an example-based color stylization of images using perceptual color categories. In this paper, we extend this method to make the algorithm more robust and to stylize the colors of video frame sequences. We present a variety of results, arguing that these images and videos convey a different, but coherent mood.

We describe a new computational approach to stylize the colors of an image by using a reference image. During processing, we take the characteristics of human color perception into account to generate more appealing results. Our system starts by classifying each pixel value into one of the basic color categories, derived from our psychophysical experiments. The basic color categories are perceptual categories that are universal to everyone, regardless of nationality or cultural background. These categories are used to provide restrictions on color transformations to avoid generating unnatural results. Our system then renders a new image by transferring colors from a reference image to the input image, based on these categorizations. To avoid artifacts due to the explicit clustering, our system defines fuzzy categorization when pseudocontours appear in the resulting image. We present a variety of results and show that our method performs a large, yet natural, color transformation without any sense of incongruity and that the resulting images automatically capture the characteristics of the colors used in the reference image.

Usually, paintings are more appealing than photographic images. This is because paintings have styles. This style can be distinguished by looking at elements such as motif, color, shape deformation and brush texture. We focus on the effect of "color" element and devise a method for transforming the color of an input photograph according to a reference painting. To do this, we consider basic color category concepts in the color transformation process. By doing so, we achieve large but natural color transformations of an image.

We describe an algorithm that generates lines of varying thickness without any special information about the lines. The thickness is derived from only curvature and lengths from both ends. Therefore the algorithm is applicable in a variety of line rendering situations, for example 3D rendering engine for high quality cel-animation-like effects, reuse of geometrical data designed by CAD for advertising purposes, and photo retouching processes with edge detection methods and so on. In addition, using the generated varying thickness, we also describe algorithms for shading and embossing line rendering.