Publications

The black box problem of artificial neural networks (ANNs) is still a very relevant issue. When communicating basic concepts of ANNs, they are often depicted as node-link diagrams. Despite this being a straight forward way to visualize them, it is rarely used outside an educational context. However, we hypothesize that large-scale node-link diagrams of full ANNs could be useful even to machine learning experts. Hence, we present a visualization tool that depicts convolutional ANNs as node-link diagrams using immersive virtual reality. We applied our tool to a use-case in the field of machine learning research and adapted it to the specific challenges. Finally, we performed an expert review to evaluate the usefulness of our visualization. We found that our node-link visualization of ANNs was perceived as helpful in this professional context.

Modeling the interactions between users and social groups of virtual agents (VAs) is vital in many virtual-reality-based applications. However, only little research on group encounters has been conducted yet. We intend to close this gap by focusing on the distinction between joining and passing-by a group. To enhance the interactive capacity of VAs in these situations, knowing the user’s objective is required to showreasonable reactions. To this end,we propose a classification scheme which infers the user’s intent based on social cues such as proxemics, gazing and orientation, followed by triggering believable, non-verbal actions on the VAs.We tested our approach in a pilot study with overall promising results and discuss possible improvements for further studies.

Generating natural embodied conversational agents within virtual spaces crucially depends on speech sounds and their directionality. In this work, we simulated directional filters to not only add directionality, but also directionally adapt each phoneme. We therefore mimic reality where changing mouth shapes have an influence on the directional propagation of sound. We conducted a study (n = 32) evaluating naturalism ratings, preference and distinguishability of omnidirectional speech auralization compared to static and dynamic, phoneme-dependent directivities. The results indicated that participants cannot distinguish dynamic from static directivity. Furthermore, participants’ preference ratings aligned with their naturalism ratings. There was no unanimity, however, with regards to which auralization is the most natural.

The authors present a virtual authoring environment for artistic creation in VR. It enables the effortless conversion of 2D images into volumetric 3D objects. Artistic elements in the input material are extracted with a convenient VR-based segmentation tool. Relief sculpting is then performed by interactively mixing different height maps. These are automatically generated from the input image structure and appearance. A prototype of the tool is showcased in an analog-virtual artistic workflow in collaboration with a traditional painter. It combines the expressiveness of analog painting and sculpting with the creative freedom of spatial arrangement in VR.

Digitalization of 3D objects and scenes using modern depth sensors and high-resolution RGB cameras enables the preservation of human cultural artifacts at an unprecedented level of detail. Interactive visualization of these large datasets, however, is challenging without degradation in visual fidelity. A common solution is to fit the dataset into available video memory by downsampling and compression. The achievable reproduction accuracy is thereby limited for interactive scenarios, such as immersive exploration in Virtual Reality (VR). This degradation in visual realism ultimately hinders the effective communication of human cultural knowledge. This article presents a method to render 3D scan datasets with minimal loss of visual fidelity. A point-based rendering approach visualizes scan data as a dense splat cloud. For improved surface approximation of thin and sparsely sampled objects, we propose oriented 3D ellipsoids as rendering primitives. To render massive texture datasets, we present a virtual texturing system that dynamically loads required image data. It is paired with a single-pass page prediction method that minimizes visible texturing artifacts. Our system renders a challenging dataset in the order of 70 million points and a texture size of 1.2 terabytes consistently at 90 frames per second in stereoscopic VR.

Tracking the temporal evolution of features in time-varying data is a key method in visualization. For typical feature definitions, such as vortices, objects are sparsely distributed over the data domain. In this paper, we present a novel approach for tracking both sparse and space-filling features. While the former comprise only a small fraction of the domain, the latter form a set of objects whose union covers the domain entirely while the individual objects are mutually disjunct. Our approach determines the assignment of features between two successive time-steps by solving two graph optimization problems. It first resolves one-to-one assignments of features by computing a maximum-weight, maximum-cardinality matching on a weighted bi-partite graph. Second, our algorithm detects events by creating a graph of potentially conflicting event explanations and finding a weighted, independent set in it. We demonstrate our method's effectiveness on synthetic and simulation data sets, the former of which enables quantitative evaluation because of the availability of ground-truth information. Here, our method performs on par or better than a well-established reference algorithm. In addition, manual visual inspection by our collaborators confirm the results' plausibility for simulation data.

Virtual-reality-based interactions with virtual agents (VAs) are likely subject to similar influences as human-human interactions. In either real or virtual social interactions, interactants try to maintain their personal space (PS), an ubiquitous, situative, flexible safety zone. Building upon larger PS preferences to humans and VAs with angry facial expressions, we extend the investigations to whole-body emotional expressions. In two immersive settings–HMD and CAVE–66 males were approached by an either happy, angry, or neutral male VA. Subjects preferred a larger PS to the angry VA when being able to stop him at their convenience (Sample task), replicating previous findings, and when being able to actively avoid him (PassBy task). In the latter task, we also observed larger distances in the CAVE than in the HMD.

In this paper, we report on the multi-year Intelligent Virtual Agents (IVA) community effort, involving more than 80 researchers worldwide, researching the IVA community interests and practises in evaluating human interaction with an artificial social agent (ASA). The effort is driven by previous IVA workshops and plenary IVA discussions related to the methodological crisis on the evaluation of ASAs. A previous literature review showed a continuous practise of creating new questionnaires instead of reusing validated questionnaires. We address this issue by examining questionnaire measurement constructs used in empirical studies between 2013 to 2018 published in the IVA conference. We identified 189 constructs used in 89 questionnaires that are reported across 81 studies. Although these constructs have different names, they often measure the same thing. In this paper, we, therefore, present a unifying set of 19 constructs that captures more than 80% of the 189 constructs initially identified. We established this set in two steps. First, 49 researchers classified the constructs in broad theoretically based categories. Next, 23 researchers grouped the constructs in each category on their similarity. The resulting 19 groups form a unifying set of constructs, which will be the basis for the future questionnaire instrument of human-ASA interaction.

Nominated for the Best Paper Award.

We present a method for example-based texturing of triangular 3D meshes. Our algorithm maps a small 2D texture sample onto objects of arbitrary size in a seamless fashion, with no visible repetitions and low overall distortion. It requires minimal user interaction and can be applied to complex, multi-layered input materials that are not required to be tileable. Our framework integrates a patch-based approach with per-pixel compositing. To minimize visual artifacts, we run a three-level optimization that starts with a rigid alignment of texture patches (macro scale), then continues with non-rigid adjustments (meso scale) and finally performs pixel-level texture blending (micro scale). We demonstrate that the relevance of the three levels depends on the texture content and type (stochastic, structured, or anisotropic textures).

the flow of virtual crowds in a direct and interactive manner. Here, options to redirect a flow by sketching barriers, or guiding entities based on a sketched network of connected sections are provided. As virtual crowds are increasingly often embedded into virtual reality (VR) applications, 3D authoring is of interest.

In this preliminary work, we thus present a sketch-based approach for VR. First promising results of a proof-of-concept are summarized and improvement suggestions, extensions, and future steps are discussed.

Since the beginning of the design and implementation of virtual environments, these systems have been built to give the users the best possible experience. One detrimental factor for the user experience was shown to be a high end-to-end latency, here measured as motionto-photon latency, of the system. Thus, a lot of research in the past was focused on the measurement and minimization of this latency in virtual environments. Most existing measurement-techniques require either expensive measurement hardware like an oscilloscope, mechanical components like a pendulum or depend on manual evaluation of samples. This paper proposes a concept of an easy to build, low-cost device consisting of a microcontroller, servo motor and a photo diode to measure the motion-to-photon latency in virtual reality environments fully automatically. It is placed or attached to the system, calibrates itself and is controlled/monitored via a web interface. While the general concept is applicable to a variety of VR technologies, this paper focuses on the context of CAVE-like systems.

To resemble realistic and lively places, virtual environments are increasingly often enriched by virtual populations consisting of computer-controlled, human-like virtual agents. While the applications often provide limited user-agent interaction based on, e.g., collision avoidance or mutual gaze, complex user-agent dynamics such as joint locomotion combined with a secondary task, e.g., conversing, are rarely considered yet. These dual-tasking situations, however, are beneficial for various use-cases: guided tours and social simulations will become more realistic and engaging if a user is able to traverse a scene as a member of a social group, while platforms to study crowd and walking behavior will become more powerful and informative. To this end, this presentation deals with different areas of interaction dynamics, which need to be combined for modeling dual-tasking with virtual agents. Areas covered are kinematic parameters for the navigation behavior, group shapes in static and mobile situations as well as verbal and non-verbal behavior for conversations.

With the advent of low-cost virtual reality hardware, new applications arise in professional contexts. These applications have requirements that can differ from the usual premise when developing immersive systems. In this work, we explore the idea that spatial controllers might not be usable for practical reasons, even though they are the best interaction device for the task. Such a reason might be fatigue, as applications might be used over a long period of time. Additionally, some people might have even more difficulty lifting their hands, due to a disability. Hence, we attempt to measure how much the performance in a spatial interaction task decreases when using classical 2D interaction devices instead of a spatial controller. For this, we developed an interaction technique that uses 2D inputs and borrows principles from desktop interaction. We show that our interaction technique is slower to use than the state-of-the-art spatial interaction but is not much worse regarding precision and user preference.

Simulating a realistic navigation of virtual pedestrians through virtual environments is a recurring subject of investigations. The various mathematical approaches used to compute the pedestrians’ paths result, i.a., in different computation-times and varying path characteristics. Customizable parameters, e.g., maximal walking speed or minimal interpersonal distance, add another level of complexity. Thus, choosing the best-fitting approach for a given environment and use-case is non-trivial, especially for novice users.

To facilitate the informed choice of a specific algorithm with a certain parameter set, crowd simulation frameworks such as Menge provide an extendable collection of approaches with a unified interface for usage. However, they often miss an elaborated visualization with high informative value accompanied by visual analysis methods to explore the complete simulation data in more detail – which is yet required for an informed choice. Benchmarking suites such as SteerBench are a helpful approach as they objectively analyze crowd simulations, however they are too tailored to specific behavior details. To this end, we propose a preliminary design of an advanced graphical user interface providing a 2D and 3D visualization of the crowd simulation data as well as features for time navigation and an overall data exploration.

Neuronal network simulators are essential to computational neuroscience, enabling the study of the nervous system through in-silico experiments. Through utilization of high-performance computing resources, these simulators are able to simulate increasingly complex and large networks of neurons today. It also creates new challenges for the analysis and visualization of such simulations. In-situ and in-transport strategies are popular approaches in these scenarios. They enable live monitoring of running simulations and parameter adjustment in the case of erroneous configurations which can save valuable compute resources.

This talk will present the current status of our pipeline for in-transport analysis and visualization of neuronal network simulator data. The pipeline is able to couple with NEST along other simulators with data management (querying, filtering and merging) from multiple simulator instances. Finally, the data is passed to end-user applications for visualization and analysis. The goal is to be integrated into third party tools such as the multi-view visual analysis toolkit ViSimpl.

Proxemic is a well known social behavioral measure, where the interpersonal distance between interactans is evaluated - either in real or in virtual social encounters. Given the prominent role of emotional expressions in our everyday social interactions, we investigated how emotions of a virtual agent affect proxemic adaptions while taking the aspects spatial constellation between user and agent as well as user’s level of dynamics into account.