Multimedia Center

Fast Collision Detection for Fracturing Rigid Bodies

By Loeiz Glondu, Sara C. Schvartzman, Maud Marchal, Georges Dumont, and Miguel A. Otaduy


In complex scenes with many objects, collision detection plays a key role in the simulation performance. This is particularly true in fracture simulation for two main reasons. One is that fracture fragments tend to exhibit very intensive contact, and the other is that collision detection data structures for new fragments need to be computed on the fly. In this paper, we present novel collision detection algorithms and data structures for real-time simulation of fracturing rigid bodies. We build on a combination of well-known efficient data structures, namely, distance fields and sphere trees, making our algorithm easy to integrate on existing simulation engines. We propose novel methods to construct these data structures, such that they can be efficiently updated upon fracture events and integrated in a simple yet effective self-adapting contact selection algorithm. Altogether, we drastically reduce the cost of both collision detection and collision response. We have evaluated our global solution for collision detection on challenging scenarios, achieving high frame rates suited for hard real-time applications such as video games or haptics. Our solution opens promising perspectives for complex fracture simulations involving many dynamically created rigid objects.

The full article can be found here:


Multivariate Network Exploration and Presentation: From Detail to Overview via Selections and Aggregations

By Stef van den Elzen and Jarke J. van Wijk

Network data is ubiquitous; e-mail traffic between persons, telecommunication, transport and financial networks are some examples. Often these networks are large and multivariate, besides the topological structure of the network, multivariate data on the nodes and links is available. Currently, exploration and analysis methods are focused on a single aspect; the network topology or the multivariate data. In addition, tools and techniques are highly domain specific and require expert knowledge. We focus on the non-expert user and propose a novel solution for multivariate network exploration and analysis that tightly couples structural and multivariate analysis. In short, we go from Detail to Overview via Selections and Aggregations (DOSA): users are enabled to gain insights through the creation of selections of interest (manually or automatically), and producing high-level, infographic-style overviews simultaneously. Finally, we present example explorations on real-world datasets that demonstrate the effectiveness of our method for the exploration and understanding of multivariate networks where presentation of findings comes for free.

The full article can be found here:


Mixed Reality Virtual Pets to Reduce Childhood Obesity

By Kyle Johnsen, Sun Joo Ahn, James Moore, Scott Brown, Thomas P. Robertson, Amanda Marable, and Aryabrata Basu


Novel approaches are needed to reduce the high rates of childhood obesity in the developed world. While multifactorial in cause, a major factor is an increasingly sedentary lifestyle of children. Our research shows that a mixed reality system that is of interest to children can be a powerful motivator of healthy activity. We designed and constructed a mixed reality system that allowed children to exercise, play with, and train a virtual pet using their own physical activity as input. The health, happiness, and intelligence of each virtual pet grew as its associated child owner exercised more, reached goals, and interacted with their pet. We report results of a research study involving 61 children from a local summer camp that shows a large increase in recorded and observed activity, alongside observational evidence that the virtual pet was responsible for that change. These results, and the ease at which the system integrated into the camp environment, demonstrate the practical potential to impact the exercise behaviors of children with mixed reality.

The full article can be found here:


GraphDiaries: Animated Transitions and Temporal Navigation for Dynamic Networks

by Benjamin Bach, Emmanuel Pietriga, and Jean-Daniel Fekete


Identifying, tracking and understanding changes in dynamic networks are complex and cognitively demanding tasks. We present GraphDiaries, a visual interface designed to improve support for these tasks in any node-link based graph visualization system. GraphDiaries relies on animated transitions that highlight changes in the network between time steps, thus helping users identify and understand those changes. To better understand the tasks related to the exploration of dynamic networks, we first introduce a task taxonomy, that informs the design of GraphDiaries, presented afterwards. We then report on a user study, based on representative tasks identified through the taxonomy, and that compares GraphDiaries to existing techniques for temporal navigation in dynamic networks, showing that it outperforms them in terms of both task time and errors for several of these tasks.

The full article can be found here:


LineUp: Visual Analysis of Multi-Attribute Rankings

by Samuel Gratzl, Alexander Lex, Nils Gehlenborg, Hanspeter Pfister, and Marc Streit


Rankings are a popular and universal approach to structuring otherwise unorganized collections of items by computing a rank for each item based on the value of one or more of its attributes. This allows us, for example, to prioritize tasks or to evaluate the performance of products relative to each other. While the visualization of a ranking itself is straightforward, its interpretation is not, because the rank of an item represents only a summary of a potentially complicated relationship between its attributes and those of the other items. It is also common that alternative rankings exist which need to be compared and analyzed to gain insight into how multiple heterogeneous attributes affect the rankings. Advanced visual exploration tools are needed to make this process efficient. In this paper we present a comprehensive analysis of requirements for the visualization of multi-attribute rankings. Based on these considerations, we propose LineUp - a novel and scalable visualization technique that uses bar charts. This interactive technique supports the ranking of items based on multiple heterogeneous attributes with different scales and semantics. It enables users to interactively combine attributes and flexibly refine parameters to explore the effect of changes in the attribute combination. This process can be employed to derive actionable insights as to which attributes of an item need to be modified in order for its rank to change. Additionally, through integration of slope graphs, LineUp can also be used to compare multiple alternative rankings on the same set of items, for example, over time or across different attribute combinations. We evaluate the effectiveness of the proposed multi-attribute visualization technique in a qualitative study. The study shows that users are able to successfully solve complex ranking tasks in a short period of time.

The full article can be found here: