Illustrative Motion Smoothing for Attention Guidance in Dynamic Visualizations

TL;DR

Illustrative Motion Smoothing for Attention Guidance in Dynamic Visualizations investigates how to guide attention in cluttered dynamic visualizations by selectively smoothing contextual motion. The approach combines geometric motion smoothing of context trajectories and visual motion smoothing via motion blur, evaluated in a crowdsourced study on dynamic molecular scenes. Key findings show that moderate visual motion smoothing improves story followability, geometric smoothing enhances aesthetics but slows perceived speed, and both techniques slow overall speed; the work provides design guidelines and highlights the need for complementary attention cues. These insights advance empirical understanding of attention guidance in dense dynamic visuals and inform animation design for complex scientific visualizations.

Abstract

3D animations are an effective method to learn about complex dynamic phenomena, such as mesoscale biological processes. The animators' goals are to convey a sense of the scene's overall complexity while, at the same time, visually guiding the user through a story of subsequent events embedded in the chaotic environment. Animators use a variety of visual emphasis techniques to guide the observers' attention through the story, such as highlighting, halos -- or by manipulating motion parameters of the scene. In this paper, we investigate the effect of smoothing the motion of contextual scene elements to attract attention to focus elements of the story exhibiting high-frequency motion. We conducted a crowdsourced study with 108 participants observing short animations with two illustrative motion smoothing strategies: geometric smoothing through noise reduction of contextual motion trajectories and visual smoothing through motion blur of \rev{context} items. We investigated the observers' ability to follow the story as well as the effect of the techniques on speed perception in a molecular scene. Our results show that moderate motion blur significantly improves users' ability to follow the story. Geometric motion smoothing is less effective but increases the visual appeal of the animation. However, both techniques also slow down the perceived speed of the animation. We discuss the implications of these results and derive design guidelines for animators of complex dynamic visualizations.

Figures (8)

• Figure 1: A short sequence of frames of a scene with extensive visual motion smoothing (trail length 4), without (a) and with (b) geometric motion smoothing. In the setting without geometric motion smoothing (with $\tau=0$ in Equation \ref{['eq:smoothMotion']}), Brownian motion leads to extensive jitter of the pink molecule's trajectory (a). With geometric motion smoothing ($\tau=1$), the pink molecule moves along a rather straight trajectory (b). Trails in the first frame were added for illustration.
• Figure 2: Snapshot of the study scene with visual motion smoothing, trail length 2. The reactants in focus are here shown in red and green in the central part of the screen.
• Figure 3: Overall percentage of correctly identified reaction pairs dependent on VMS trail length, grouped by GMS (non-smoothed $\tau=0$ in blue and smooth $\tau=1$ in orange).
• Figure 4: How difficult it was considered by users to spot a reaction dependent on VMS trail length, grouped by GMS.
• Figure 5: Estimated speed dependent on ground truth speed, grouped by GMS.