Line Integral Convolution (LIC) is a powerful technique for gener- ating striking images and animations from vector data. Introduced in 1993, the method has rapidly found many application areas, rang- ing from computer arts to scientific visualization. Based upon lo- cally filtering an input texture along a curved stream line segment in a vector field, it is able to depict directional information at high spatial resolutions. We present a new method for computing LIC images. It em- ploys simple box filter kernels only and minimizes the total num- ber of stream lines to be computed. Thereby it reduces computa- tional costs by an order of magnitude compared to the original algo- rithm. Our method utilizes fast, error-controlled numerical integra- tors. Decoupling the characteristic lengths in vector field grid, input texture and output image, it allows computation of filtered images at arbitrary resolution. This feature is of significance in computer animation as well as in scientific visualization, where it can be used to explore vector data by smoothly enlarging structure of details. We also present methods for improved texture animation, again employing box filter kernels only. To obtain an optimal motion ef- fect, spatial decay of correlation between intens ities of distant pixels in the output image has to be controlled. This is achieved by blend- ing different phase-shifted box filter animations and by adaptively rescaling the contrast of the output frames.