The urgent need to improve health communication is highlighted by the millions of premature deaths worldwide each year due to lifestyle choices and behavioral risks. These losses reveal that researching and understanding these risks is not sufficient; we must also communicate them effectively to the public. In this paper, we discuss how we can assist experts in creating data-based risk visualizations for the general public. Our tool, RACCOON, is able to identify and suggest the most important risk factors in a data set, visualizing them in a way that allows seamless exploration of the data set. Then, we use the latest research in risk communication, narrative visualization, and affective visualization to generate engaging visualizations for the general public. Extensive customization options allow the expert to integrate their domain knowledge, and tailor the visualizations to their data story and communicative intent. We evaluated RACCOON with domain experts, as well as our visualizations with the general public. The findings highlight RACCOON's effectiveness in providing intuitive and engaging visualizations that appeal to a broad audience. They also provide first insights into the interplay of visualization design and communicative intent. By fusing the research fields of risk communication, narrative visualization, and affective visualization in one visualization generation tool, we provide a novel approach to support domain experts in communicating risks and risk factors to the general public.
Recirculation in flows is an important phenomenon of dynamical systems as it is linked to numerous further properties and behaviors. A formal definition of recirculation surfaces has been introduced in previous work. However, the extraction and visualization of such surfaces is a highly complex challenge as they are 2-manifolds in the 5D space. Although an approach for the geometry extraction exists, there are still several unsolved problems, mainly connected to the computational effort and surface reconstruction. In this work, we propose a fundamentally different idea: Instead of extracting an explicit geometry, we apply a direct ray tracing approach. This way, we effectively circumvent the challenge of reconstructing the geometry. Additionally, we implement multiple strategies for an efficient computation. Due to this, we are able to provide a visualization of recirculation surfaces in a fraction of the computation time of existing approaches.
Data items arranged into groups form partitions, and across time or through variation of grouping criteria, those partitions may change. While alluvial diagrams, showing the flow of data items as streams, visually capture such changes in partition sequences, their focus on showing similarities between neighboring partitions limits their application. Our paper introduces novel augmentations of alluvial diagrams with interactive visualizations and linked analysis, explicitly targeting the comparison of non-neighboring partitions without sacrificing the sequential nature of the data. Juxtaposed visualizations with the alluvial diagram's timeline provide a comparison of a selected partition to all other partitions, while additional scatterplot views provide an overview of the partition and set similarities. Connecting the set representations across views, we propose a coloring approach of sets and interactive selection mechanisms. The usefulness and generalizability of the approach are demonstrated through examples with application in supervised and unsupervised machine learning, as well as work collaboration analysis.
We present COMAND, a novel method for controllable multi-action 3D motion synthesis without requiring action-labeled data. Our method can generate a lifelike motion sequence containing consecutive non-locomotive actions such as kicking, jumping, or squatting, without the need for manual blending, enabling an intuitive way to control 3D human animation based on the desired motion types at specified time windows. At the core of our method is a motion manifold based on a periodic parameterization of a motion latent space that allows for unsupervised action clustering of 3D motion, thus allowing action-to-motion synthesis without the need to explicitly train the model on action-labeled datasets. This learned motion manifold has semantic and periodic properties that benefit 3D motion synthesis from action labels and from free-form text input, resulting in a state-of-the-art multi-modal and multi-action 3D motion generation framework. Our study shows that more than 83% and 96% of the users respectively rated COMAND as more natural and better matching the target action sequence when compared to existing methods.
Many visualization techniques exist for interactive exploration of segmentation volumes, however, photorealistic renderings are usually computed using slow offline techniques. We present a novel compression technique for segmentation volumes which enables interactive path tracing-based visualization for datasets up to hundreds of gigabytes: For every label, we create a grid of fixed-size axis aligned bounding boxes (AABBs) which covers the occupied voxels. For each AABB we first construct a sparse voxel octree (SVO) representing the contained voxels of the respective label, and then build a sparse voxel directed acyclic graph (SVDAG) identifying identical sub-trees across all SVOs; the lowest tree levels are stored as an occupancy bit-field. As a last step, we build a bounding volume hierarchy for the AABBs as a spatial indexing structure. Our representation solves a compression rate limitation of related SVDAG works as labels only need to be stored along with each AABB and not in the graph encoding of their shape. Our compression is GPU-friendly as hardware raytracing efficiently finds AABB intersections which we then traverse using a custom accelerated SVDAG traversal. Our method is able to path-trace a 113 GB volume on a consumer-grade GPU with 1 sample per pixel with up to 32 bounces at 108 FPS in a lossless representation, or at up to 1017 FPS when using dynamic level of detail.
