Publications

In this paper, we address the problem of object discovery in time-varying, large-scale image collections. A core part of our approach is a novel Limited Horizon Minimum Spanning Tree (LH-MST) structure that closely approximates the Minimum Spanning Tree at a small fraction of the latter’s computational cost. Our proposed tree structure can be created in a local neighborhood of the matching graph during image retrieval and can be efficiently updated whenever the image database is extended. We show how the LH-MST can be used within both single-link hierarchical agglomerative clustering and the Iconoid Shift framework for object discovery in image collections, resulting in significant efficiency gains and making both approaches capable of incremental clustering with online updates. We evaluate our approach on a dataset of 500k images from the city of Paris and compare its results to the batch version of both clustering algorithms.

In this paper we present a novel method for non-linear shape opti- mization of 3d objects given by their surface representation. Our method takes advantage of the fact that various shape properties of interest give rise to underdetermined design spaces implying the existence of many good solutions. Our algorithm exploits this by performing iterative projections of the problem to local subspaces where it can be solved much more efficiently using standard numer- ical routines. We demonstrate how this approach can be utilized for various shape optimization tasks using different shape parameteri- zations. In particular, we show how to efficiently optimize natural frequencies, mass properties, as well as the structural yield strength of a solid body. Our method is flexible, easy to implement, and very fast.

State-of-the-art hex meshing algorithms consist of three steps: Frame-field design, parametrization generation, and mesh extraction. However, while the first two steps are usually discussed in detail, the last step is often not well studied. In this paper, we fully concentrate on reliable mesh extraction.

Parametrization methods employ computationally expensive countermeasures to avoid mapping input tetrahedra to degenerate or flipped tetrahedra in the parameter domain because such a parametrization does not define a proper hexahedral mesh. Nevertheless, there is no known technique that can guarantee the complete absence of such artifacts.

We tackle this problem from the other side by developing a mesh extraction algorithm which is extremely robust against typical imperfections in the parametrization. First, a sanitization process cleans up numerical inconsistencies of the parameter values caused by limited precision solvers and floating-point number representation. On the sanitized parametrization, we extract vertices and so-called darts based on intersections of the integer grid with the parametric image of the tetrahedral mesh. The darts are reliably interconnected by tracing within the parametrization and thus define the topology of the hexahedral mesh. In a postprocessing step, we let certain pairs of darts cancel each other, counteracting the effect of flipped regions of the parametrization. With this strategy, our algorithm is able to robustly extract hexahedral meshes from imperfect parametrizations which previously would have been considered defective. The algorithm will be published as an open source library.

Pa­ra­met­ri­za­tion based methods have recently become very popular for the generation of high quality quad meshes. In contrast to previous approaches, they allow for intuitive user control in order to accommodate all kinds of application driven constraints and design intentions. A major obstacle in practice, however, are the relatively long computations that lead to response times of several minutes already for input models of moderate complexity. In this paper we introduce a novel strategy to handle highly complex input meshes with up to several millions of triangles such that quad meshes can still be created and edited within an interactive workflow. Our method is based on representing the input model on different levels of resolution with a mechanism to propagate pa­ra­met­ri­za­tions from coarser to finer levels. The major challenge is to guarantee consistent pa­ra­met­ri­za­tions even in the presence of charts, transition functions, and singularities. Moreover, the remaining degrees of freedom on coarser levels of resolution have to be chosen carefully in order to still achieve low distortion pa­ra­met­ri­za­tions. We demonstrate a prototypic system where the user can interactively edit quad meshes with powerful high-level operations such as guiding constraints, singularity repositioning, and singularity connections.

The typical goal of surface remeshing consists in finding a mesh that is (1) geometrically faithful to the original geometry, (2) as coarse as possible to obtain a low-complexity representation and (3) free of bad elements that would hamper the desired application. In this paper, we design an algorithm to address all three optimization goals simultaneously. The user specifies desired bounds on approximation error (delta), minimal interior angle (theta) and maximum mesh complexity N (number of vertices). Since such a desired mesh might not even exist, our optimization framework treats only the approximation error bound (delta) as a hard constraint and the other two criteria as optimization goals. More specifically, we iteratively perform carefully prioritized local operators, whenever they do not violate the approximation error bound and improve the mesh otherwise. Our optimization framework greedily searches for the coarsest mesh with minimal interior angle above (theta) and approximation error bounded by (delta). Fast runtime is enabled by a local approximation error estimation, while implicit feature preservation is obtained by specifically designed vertex relocation operators. Experiments show that our approach delivers high-quality meshes with implicitly preserved features and better balances between geometric fidelity, mesh complexity and element quality than the state-of-the-art.

Direction fields and vector fields play an increasingly important role in computer graphics and geometry processing. The synthesis of directional fields on surfaces, or other spatial domains, is a fundamental step in numerous applications, such as mesh generation, deformation, texture mapping, and many more. The wide range of applications resulted in definitions for many types of directional fields: from vector and tensor fields, over line and cross fields, to frame and vector-set fields. Depending on the application at hand, researchers have used various notions of objectives and constraints to synthesize such fields. These notions are defined in terms of fairness, feature alignment, symmetry, or field topology, to mention just a few. To facilitate these objectives, various representations, discretizations, and optimization strategies have been developed. These choices come with varying strengths and weaknesses. This report provides a systematic overview of directional field synthesis for graphics applications, the challenges it poses, and the methods developed in recent years to address these challenges.

ConvNet training is highly sensitive to initialization of the weights. A widespread approach is to initialize the network with weights trained for a different task, an auxiliary task. The ImageNet-based ILSVRC classification task is a very popular choice for this, as it has shown to produce powerful feature representations applicable to a wide variety of tasks. However, this creates a significant entry barrier to exploring non-standard architectures. In this paper, we propose a self-supervised pretraining, the PatchTask, to obtain weight initializations for fine-grained recognition problems, such as person attribute recognition, pose estimation, or action recognition. Our pretraining allows us to leverage additional unlabeled data from the same source, which is often readily available, such as detection bounding boxes. We experimentally show that our method outperforms a standard random initialization by a considerable margin and closely matches the ImageNet-based initialization.

Tracking in urban street scenes is predominantly based on pretrained object-specific detectors and Kalman filter based tracking. More recently, methods have been proposed that track objects by modelling their shape, as well as ones that predict the motion of ob- jects using learned trajectory models. In this paper, we combine these ideas and propose shape-motion patterns (SMPs) that incorporate shape as well as motion to model a vari- ety of objects in an unsupervised way. By using shape, our method can learn trajectory models that distinguish object categories with distinct behaviour. We develop methods to classify objects into SMPs and to predict future motion. In experiments, we analyze our learned categorization and demonstrate superior performance of our motion predictions compared to a Kalman filter and a learned pure trajectory model. We also demonstrate how SMPs can indicate potentially harmful situations in traffic scenarios.

Scene understanding is an important prerequisite for vehicles and robots that operate autonomously in dynamic urban street scenes. For navigation and high-level behavior planning, the robots not only require a persistent 3D model of the static surroundings - equally important, they need to perceive and keep track of dynamic objects. In this paper, we propose a method that incrementally fuses stereo frame observations into temporally consistent semantic 3D maps. In contrast to previous work, our approach uses scene flow to propagate dynamic objects within the map. Our method provides a persistent 3D occupancy as well as semantic belief on static as well as moving objects. This allows for advanced reasoning on objects despite noisy single-frame observations and occlusions. We develop a novel approach to discover object instances based on the temporally consistent shape, appearance, motion, and semantic cues in our maps. We evaluate our approaches to dynamic semantic mapping and object discovery on the popular KITTI benchmark and demonstrate improved results compared to single-frame methods.

Estimating the pose and 3D shape of a large variety of instances within an object class from stereo images is a challenging problem, especially in realistic conditions such as urban street scenes. We propose a novel approach for using compact shape manifolds of the shape within an object class for object segmentation, pose and shape estimation. Our method first detects objects and estimates their pose coarsely in the stereo images using a state-of-the-art 3D object detection method. An energy minimization method then aligns shape and pose concurrently with the stereo reconstruction of the object. In experiments, we evaluate our approach for detection, pose and shape estimation of cars in real stereo images of urban street scenes. We demonstrate that our shape manifold alignment method yields improved results over the initial stereo reconstruction and object detection method in depth and pose accuracy.

Most vision based systems for object tracking in urban environments focus on a limited number of important object categories such as cars or pedestrians, for which powerful detectors are available. However, practical driving scenarios contain many additional objects of interest, for which suitable detectors either do not yet exist or would be cumbersome to obtain. In this paper we propose a more general tracking approach which does not follow the often used tracking-by- detection principle. Instead, we investigate how far we can get by tracking unknown, generic objects in challenging street scenes. As such, we do not restrict ourselves to only tracking the most common categories, but are able to handle a large variety of static and moving objects. We evaluate our approach on the KITTI dataset and show competitive results for the annotated classes, even though we are not restricted to them.

Feature curves on surface meshes are usually defined solely based on local shape properties such as dihedral angles and principal curvatures. From the application perspective, however, the meaningfulness of a network of feature curves also depends on a global scale parameter that takes the distance between feature curves into account, i.e., on a coarse scale, nearby feature curves should be merged or suppressed if the surface region between them is not representable at the given scale/resolution. In this paper, we propose a computational approach to the intuitive notion of scale conforming feature curve networks where the density of feature curves on the surface adapts to a global scale parameter. We present a constrained global optimization algorithm that computes scale conforming feature curve networks by eliminating curve segments that represent surface features, which are not compatible to the prescribed scale. To demonstrate the usefulness of our approach we apply isotropic and anisotropic remeshing schemes that take our feature curve networks as input. For a number of example meshes, we thus generate high quality shape approximations at various levels of detail.

We present a pipeline of algorithms that decomposes a given polygon model into parts such that each part can be 3D printed with high (outer) surface quality. For this we exploit the fact that most 3D printing technologies have an anisotropic resolution and hence the surface smoothness varies significantly with the orientation of the surface. Our pipeline starts by segmenting the input surface into patches such that their normals can be aligned perpendicularly to the printing direction. A 3D Voronoi diagram is computed such that the intersections of the Voronoi cells with the surface approximate these surface patches. The intersections of the Voronoi cells with the input model's volume then provide an initial decomposition. We further present an algorithm to compute an assembly order for the parts and generate connectors between them. A post processing step further optimizes the seams between segments to improve the visual quality. We run our pipeline on a wide range of 3D models and experimentally evaluate the obtained improvements in terms of numerical, visual, and haptic quality.

Planes are predominant features of man-made environments which have been exploited in many mapping approaches. In this paper, we propose a real-time capable RGB-D SLAM system that consistently integrates frame-to-keyframe and frame-to-plane alignment. Our method models the environment with a global plane model and – besides direct image alignment – it uses the planes for tracking and global graph optimization. This way, our method makes use of the dense image information available in keyframes for accurate short-term tracking. At the same time it uses a global model to reduce drift. Both components are integrated consistently in an expectation-maximization framework. In experiments, we demonstrate the benefits our approach and its state-of-the-art accuracy on challenging benchmarks.

We propose a novel direct visual-inertial odometry method for stereo cameras. Camera pose, velocity and IMU biases are simultaneously estimated by minimizing a combined photometric and inertial energy functional. This allows us to exploit the complementary nature of vision and inertial data. At the same time, and in contrast to all existing visual-inertial methods, our approach is fully direct: geometry is estimated in the form of semi-dense depth maps instead of manually designed sparse keypoints. Depth information is obtained both from static stereo – relating the fixed-baseline images of the stereo camera – and temporal stereo – relating images from the same camera, taken at different points in time. We show that our method outperforms not only vision-only or loosely coupled approaches, but also can achieve more accurate results than state-of-the-art keypoint-based methods on different datasets, including rapid motion and significant illumination changes. In addition, our method provides high-fidelity semi-dense, metric reconstructions of the environment, and runs in real-time on a CPU.

We present a novel method to simulate bending and torsion of elastic rods within the position-based dynamics (PBD) framework. The main challenge is that torsion effects of Cosserat rods are described in terms of material frames which are attached to the centerline of the rod. But frames or orientations do not fit into the classical position-based dynamics formulation. To solve this problem we introduce new types of constraints to couple orientations which are represented by unit quaternions. For constraint projection quaternions are treated in the exact same way as positions. Unit length is enforced with an additional constraint. This allows us to use the strain measures form Cosserat theory directly as constraints in PBD. It leads to very simple algebraic expressions for the correction displacements which only contain quaternion products and additions. Our results show that our method is very robust and accurately produces the complex bending and torsion effects of rods. Due to its simplicity our method is very efficient and more than one order of magnitude faster than existing position-based rod simulation methods. It even achieves the same performance as position-based simulations without torsion effects.

Cognitive service robots that shall assist persons in need of performing their activities of daily living have recently received much attention in robotics research. Such robots require a vast set of control and perception capabilities to provide useful assistance through mobile manipulation and human–robot interaction. In this article, we present hardware design, perception, and control methods for our cognitive service robot Cosero. We complement autonomous capabilities with handheld teleoperation interfaces on three levels of autonomy. The robot demonstrated various advanced skills, including the use of tools. With our robot, we participated in the annual international RoboCup@Home competitions, winning them three times in a row.

Modern mobile phones can capture and process high quality videos, which makes them a very popular tool to create and watch video content. However when watching a video together with a group, it is not convenient to watch on one mobile display due to its small form factor. One idea is to combine multiple mobile displays together to create a larger interactive surface for sharing visual content. However so far a practical framework supporting synchronous video playback on multiple mobile displays is still missing. We present the design of “MobileVideoTiles”, a mobile application that enables users to watch local or online videos on a big virtual screen composed of multiple mobile displays. We focus on improving video quality and usability of the tiled virtual screen. The major technical contributions include: mobile peer-to-peer video streaming, playback synchronization, and accessibility of video resources. The prototype application has got several thousand downloads since release and re ceived very positive feedback from users.

In this paper we propose a novel method to construct hierarchical $hp$-adaptive Signed Distance Fields (SDFs). We discretize the signed distance function of an input mesh using piecewise polynomials on an axis-aligned hexahedral grid. Besides spatial refinement based on octree subdivision to refine the cell size (h), we hierarchically increase each cell's polynomial degree (p) in order to construct a very accurate but memory-efficient representation. Presenting a novel criterion to decide whether to apply h- or p-refinement, we demonstrate that our method is able to construct more accurate SDFs at significantly lower memory consumption than previous approaches. Finally, we demonstrate the usage of our representation as collision detector for geometrically highly complex solid objects in the application area of physically-based simulation.

Various applications of global surface parametrization benefit from the alignment of parametrization isolines with principal curvature directions. This is particularly true for recent parametrization-based meshing approaches, where this directly translates into a shape-aware edge flow, better approximation quality, and reduced meshing artifacts. Existing methods to influence a parametrization based on principal curvature directions suffer from scale-dependence, which implies the necessity of parameter variation, or try to capture complex directional shape features using simple 1D curves. Especially for non-sharp features, such as chamfers, fillets, blends, and even more for organic variants thereof, these abstractions can be unfit. We present a novel approach which respects and exploits the 2D nature of such directional feature regions, detects them based on coherence and homogeneity properties, and controls the parametrization process accordingly. This approach enables us to provide an intuitive, scale-invariant control parameter to the user. It also allows us to consider non-local aspects like the topology of a feature, enabling further improvements. We demonstrate that, compared to previous approaches, global parametrizations of higher quality can be generated without user intervention.

We present a method that expands on previous work in learning human perceived style similarity across objects with different structures and functionalities. Unlike previous approaches that tackle this problem with the help of hand-crafted geometric descriptors, we make use of recent advances in metric learning with neural networks (deep metric learning). This allows us to train the similarity metric on a shape collection directly, since any low- or high-level features needed to discriminate between different styles are identified by the neural network automatically. Furthermore, we avoid the issue of finding and comparing sub-elements of the shapes. We represent the shapes as rendered images and show how image tuples can be selected, generated and used efficiently for deep metric learning. We also tackle the problem of training our neural networks on relatively small datasets and show that we achieve style classification accuracy competitive with the state of the art. Finally, to reduce annotation effort we propose a method to incorporate heterogeneous data sources by adding annotated photos found online in order to expand or supplant parts of our training data.

Common approaches for the haptic rendering of complex scenarios employ multi-rate simulation schemes. Here, the collision queries or the simulation of a complex deformable object are often performed asynchronously at a lower frequency, while some kind of intermediate contact representation is used to simulate interactions at the haptic rate. However, this can produce artifacts in the haptic rendering when the contact situation quickly changes and the intermediate representation is not able to reflect the changes due to the lower update rate.

We address this problem utilizing a novel contact model. It facilitates the creation of contact representations that are accurate for a large range of motions and multiple simulation time-steps. We handle problematic geometrically convex contact regions using a local convex decomposition and special constraints for convex areas. We combine our accurate contact model with an implicit temporal integration scheme to create an intermediate mechanical contact representation, which reflects the dynamic behavior of the simulated objects. To maintain a haptic real time simulation, the size of the region modeled by the contact representation is automatically adapted to the complexity of the geometry in contact. Moreover, we propose a new iterative solving scheme for the involved constrained dynamics problems. We increase the robustness of our method using techniques from trust region-based optimization. Our approach can be combined with standard methods for the modeling of deformable objects or constraint-based approaches for the modeling of, for instance, friction or joints. We demonstrate its benefits with respect to the simulation accuracy and the quality of the rendered haptic forces in several scenarios with one or more haptic proxies.

Interactive analysis of 3D relational data is challenging. A common way of representing such data are node-link diagrams as they support analysts in achieving a mental model of the data. However, naïve 3D depictions of complex graphs tend to be visually cluttered, even more than in a 2D layout. This makes graph exploration and data analysis less efficient. This problem can be addressed by edge bundling. We introduce a 3D cluster-based edge bundling algorithm that is inspired by the force-directed edge bundling (FDEB) algorithm [Holten2009] and fulfills the requirements to be embedded in an interactive framework for spatial data analysis. It is parallelized and scales with the size of the graph regarding the runtime. Furthermore, it maintains the edge’s model and thus supports rendering the graph in different structural styles. We demonstrate this with a graph originating from a simulation of the function of a macaque brain.

To avoid simulator sickness and improve presence in immersive virtual environments (IVEs), high frame rates and low latency are required. In contrast, volume rendering applications typically strive for high visual quality that induces high computational load and, thus, leads to low frame rates. To evaluate this trade-off in IVEs, we conducted a controlled user study with 53 participants. Search and count tasks were performed in a CAVE with varying volume rendering conditions which are applied according to viewer position updates corresponding to head tracking. The results of our study indicate that participants preferred the rendering condition with continuous adjustment of the visual quality over an instantaneous adjustment which guaranteed for low latency and over no adjustment providing constant high visual quality but rather low frame rates. Within the continuous condition, the participants showed best task performance and felt less disturbed by effects of the visualization during movements. Our findings provide a good basis for further evaluations of how to accelerate volume rendering in IVEs according to user’s preferences.

When moving through a tracked immersive virtual environment, it is sometimes useful to deviate from the normal one-to-one mapping of real to virtual motion. One option is the application of rotation gain, where the virtual rotation of a user around the vertical axis is amplified or reduced by a factor. Previous research in head-mounted display environments has shown that rotation gain can go unnoticed to a certain extent, which is exploited in redirected walking techniques. Furthermore, it can be used to increase the effective field of regard in projection systems. However, rotation gain has never been studied in CAVE systems, yet. In this work, we present an experiment with 87 participants examining the effects of rotation gain in a CAVE-like virtual environment. The results show no significant effects of rotation gain on simulator sickness, presence, or user performance in a cognitive task, but indicate that there is a negative influence on spatial knowledge especially for inexperienced users. In secondary results, we could confirm results of previous work and demonstrate that they also hold for CAVE environments, showing a negative correlation between simulator sickness and presence, cognitive performance and spatial knowledge, a positive correlation between presence and spatial knowledge, a mitigating influence of experience with 3D applications and previous CAVE exposure on simulator sickness, and a higher incidence of simulator sickness in women.

Data annotation finds increasing use in Virtual Reality applications with the goal to support the data analysis process, such as architectural reviews. In this context, a variety of different annotation systems for application to immersive virtual environments have been presented. While many interesting interaction designs for the data annotation workflow have emerged from them, important details and evaluations are often omitted. In particular, we observe that the process of handling metadata to interactively create and manage complex annotations is often not covered in detail. In this paper, we strive to improve this situation by focusing on the design of data annotation workflows and their evaluation. We propose a workflow design that facilitates the most important annotation operations, i.e., annotation creation, review, and modification. Our workflow design is easily extensible in terms of supported annotation and metadata types as well as interaction techniques, which makes it suitable for a variety of application scenarios. To evaluate it, we have conducted a user study in a CAVE-like virtual environment in which we compared our design to two alternatives in terms of a realistic annotation creation task. Our design obtained good results in terms of task performance and user experience.

This article wants to give some impulses for a discussion about how an “ultimate” display should look like to support the Neuroscience community in an optimal way. In particular, we will have a look at immersive display technology. Since its hype in the early 90’s, immersive Virtual Reality has undoubtedly been adopted as a useful tool in a variety of application domains and has indeed proven its potential to support the process of scientific data analysis. Yet, it is still an open question whether or not such non-standard displays make sense in the context of neuroscientific data analysis. We argue that the potential of immersive displays is neither about the raw pixel count only, nor about other hardware-centric characteristics. Instead, we advocate the design of intuitive and powerful user interfaces for a direct interaction with the data, which support the multi-view paradigm in an efficient and flexible way, and – finally – provide interactive response times even for huge amounts of data and when dealing multiple datasets simultaneously.

With the increase in data availability and data volume it becomes increasingly important to extract information and actionable knowledge from data. Information Visualization helps the user to understand data by utilizing vision as a relatively parallel input channel to the user’s mind. Decision Support systems on the other hand help users in making information actionable, by suggesting beneficial decisions and presenting them in context. Both fields share a common need for understanding the interface between the computer and the human. This makes human factors research critical for both fields. Understanding limitations of human perception, cognition and action, as well as their variance must be understood to fully leverage information visualization and decision support. This article reflects on research agendas for investigating human factors in the aforementioned fields.

We present a city reconstruction and visualization framework that integrates geometric models reconstructed with a range of different techniques. The framework generates the vast majority of buildings procedurally, which yields plausible visualizations for structurally simple buildings, e.g. residential buildings. For structurally complex landmarks, e.g. churches, a procedural approach does not achieve satisfactory visual fidelity. Thus, we also employ image-based techniques to reconstruct the latter in a more realistic, recognizable way. As the manual acquisition of data required for the procedural and image-based reconstructions is practically infeasible for whole cities, we rely on publicly available data as well as crowd sourcing projects. This enables our framework to render views from cities without any dedicated data acquisition as long as there are sufficient public data sources available. To obtain a more lively impression of a city, we also visualize dynamic features like weather conditions and traffic based on publicly available real-time data.

During the last decade a set of surface descriptors have been presented describing local surface features. Recent approaches have shown that augmenting local descriptors with topological information improves the correspondence and segmentation quality. In this paper we build upon the work of Tevs et al. and Sun and Abidi by presenting a surface descriptor which captures both local surface properties and topological features of 3D objects. We present experiments on shape repositories that are provided with ground-truth correspondences (FAUST, SCAPE, TOSCA) which show that this descriptor outperforms current local surface descriptors.

We introduce deferred warping, a novel approach for real-time deformation of 3D objects attached to an animated or manipulated surface. Our target application is virtual prototyping of garments where 2D pattern modeling is combined with 3D garment simulation which allows an immediate validation of the design. The technique works in two steps: First, the surface deformation of the target object is determined and the resulting transformation field is stored as a matrix texture. Then the matrix texture is used as look-up table to transform a given geometry onto a deformed surface. Splitting the process in two steps yields a large flexibility since different attachment types can be realized by simply defining specific mapping functions. Our technique can directly handle complex topology changes within the surface. We demonstrate a fast implementation in the vertex shading stage allowing the use of highly decorated surfaces with millions of triangles in real-time.

Provenance tracking for visual analysis workflows is still a challenge as especially interaction and collaboration aspects are poorly covered in existing realizations. Therefore, we propose a first prototype addressing these issues based on the PROV model. Interactions in multiple applications by multiple users can be tracked by means of a web interface and, thus, allowing even for tracking of remote-located collaboration partners. In the end, we demonstrate the applicability based on two use cases and discuss some open issues not addressed by our implementation so far but that can be easily integrated into our architecture.

An immersive virtual environment is the ideal platform for the planning and training of on-orbit servicing missions. In such kind of virtual assembly simulation, grasping virtual objects is one of the most common and natural interactions. In this paper, we present a novel, small and lightweight electrotactile feedback device, specifically designed for immersive virtual environments. We conducted a study to assess the feasibility and usability of our interaction device. Results show that electrotactile feedback improved the user’s grasping in our virtual on-orbit servicing scenario. The task completion time was significantly lower and the precision of the user’s interaction was higher.

Computer-controlled, human-like virtual agents (VAs), are often embedded into immersive virtual environments (IVEs) in order to enliven a scene or to assist users. Certain constraints need to be fulfilled, e.g., a collision avoidance strategy allowing users to maintain their personal space. Violating this flexible protective zone causes discomfort in real-world situations and in IVEs. However, no studies on collision avoidance for small-scale IVEs have been conducted yet.

Our goal is to close this gap by presenting the results of a controlled user study in a CAVE. 27 participants were immersed in a small-scale office with the task of reaching the office door. Their way was blocked either by a male or female VA, representing their co-worker. The VA showed different behavioral patterns regarding gaze and locomotion. Our results indicate that participants preferred collaborative collision avoidance: they expect the VA to step aside in order to get more space to pass while being willing to adapt their own walking paths.

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