The expressive power of Chinese calligraphy lies in its gestural dynamics, such as rhythm and velocity, which are implicitly captured in the final 2D form. This paper introduces Generative Strokes, a novel parametric framework that translates these implicit dynamics into explicit and controllable three-dimensional geometry. Our approach models a stroke from its boundary contours, constructing a volumetric representation through a multi-stage generative pipeline. The framework establishes an internal structural scaffold, computes a depth profile for the boundaries based on the integral of their local angular changes, and generates a continuous internal surface by enforcing a geometric orthogonality constraint. This process encodes gestural flow directly into the stroke’s 3D form. Key parameters for depth intensity, flow smoothing, and surface curvature provide direct control over the geometric expression of gestural qualities. This framework offers a new methodology for digital cultural heritage, enabling (1) the quantitative analysis of calligraphic styles, (2) the generation of novel 3D calligraphic sculptures rooted in traditional aesthetics, and (3) new possibilities for interactive educational tools that make the intangible aspects of calligraphy more accessible.

In this paper, we study the visual design of hierarchical multivariate data analysis. We focus on the extension of four hierarchical univariate concepts-the sunburst chart, the icicle plot, the circular treemap, and the bubble treemap-to the multivariate domain. Our study identifies several advantageous design variants, which we discuss with respect to previous approaches, and whose utility we evaluate with a user study and demonstrate for different analysis purposes and different types of data.

In this paper, we introduce uncertainty to continuous scatterplots and continuous parallel coordinates. We derive respective models, validate them with sampling-based brute-force schemes, and present acceleration strategies for their computation. At the same time, we show that our approach lends itself as well for introducing uncertainty into the definition of fibers in bivariate data. Finally, we demonstrate the properties and the utility of our approach using specifically designed synthetic cases and simulated data.

Abstract In this paper, we show how the equivalence property leads to the novel concept of equivalent regions in mappings from ℝn to ℝn. We present a technique for obtaining these regions both in the domain and the codomain of such a mapping, and determine their correspondence. This enables effective investigation of variation equivalence within mappings, and between mappings in terms of comparative visualization. We implement our approach for n = 2, and demonstrate its utility using different examples.