Improper technique is detrimental to a tennis player's performance and increases the risk of injury. Regular training routines are necessary but take effort and motivation, and on-court training requires resources that pose additional barriers. Virtual reality paves the way for engaging self-training applications that address these barriers, but without a coach, motion errors may go uncorrected. We present a complementary way of practicing aspects of proper tennis forehand technique in virtual reality, utilizing automated motion analysis for immediate post-action multimodal feedback. Our VR tennis training utilizes motor learning principles and motion analysis to reinforce proper movement patterns and provide timely corrections. We overcome the problem of complex motion analysis by breaking the motion into distinct phases and utilizing the concept of coaching rules. After each shot, auditory and visual feedback is given, focusing on one aspect at a time. The exclusive use of the Meta Quest's partial motion capture poses technical challenges, restricting the set of applicable coaching rules and feedback due to the limited number of tracked joints. However, it allows us to present a more accessible and flexible alternative to on-court training and existing self-training setups. We conducted a user study$(\mathrm{N} = 26)$following a within-subjects pretest-posttest design to evaluate short-term effects of our VR tennis training. Results demonstrate significant improvements in motivation, performance metrics, and participants' self-reported confidence in technique from the pre- to posttest, suggesting a potential short-term learning effect. Qualitative insights reveal that participants believe our VR training can complement traditional tennis training to a certain degree.

Abstract In this paper, we present OffiStretch, a camera-based system for optimal stretching guidance at home or in the workplace. It consists of a vision-based method for real-time assessment of the user’s body pose to provide visual feedback as interactive guidance during stretching exercises. Our method compares the users’ actual pose with a pre-trained target pose to assess the quality of stretching for a number of different exercises. We utilize angular and spatial pose features to perform this comparison for each individual exercise. The result of this pose assessment is presented to the user as real-time visual feedback on an "augmented mirror" display. As our method relies simply on a single RGB camera, it can be easily utilized in everyday training scenarios. We validate our method in a user study, comparing users’ performance and motivation in stretching when receiving audio-visual guidance on a TV screen both with and without our live feedback. While participants performed equally well in both conditions, feedback boosted their motivation to perform the exercises, highlighting its potential for increasing users’ well-being. Moreover, our results suggest that participants preferred stretching exercises with our live feedback over the condition without the feedback. Finally, an expert evaluation with professional physiotherapists reveals that further work must target improvements of the feedback to ensure correct guidance during stretching.

Adaptive user interfaces can improve experiences in Extended Reality (XR) applications by adapting interface elements according to the user’s context. Although extensive work explores different adaptation policies, XR creators often struggle with their implementation, which involves laborious manual scripting. The few available tools are underdeveloped for realistic XR settings where it is often necessary to consider conflicting aspects that affect an adaptation. We fill this gap by presenting AUIT, a toolkit that facilitates the design of optimization-based adaptation policies. AUIT allows creators to flexibly combine policies that address common objectives in XR applications, such as element reachability, visibility, and consistency. Instead of using rules or scripts, specifying adaptation policies via adaptation objectives simplifies the design process and enables creative exploration of adaptations. After creators decide which adaptation objectives to use, a multi-objective solver finds appropriate adaptations in real-time. A study showed that AUIT allowed creators of XR applications to quickly and easily create high-quality adaptations.

This paper presents a novel method for the visualization of 3D spatial sounds in Virtual Reality (VR) for Deaf and Hard-of-Hearing (DHH) people. Our method enhances traditional VR devices with additional haptic and visual feedback, which aids spatial sound localization. The proposed system automatically analyses 3D sound from VR application, and it indicates the direction of sound sources to a user by two Vibro-motors and two Light-Emitting Diodes (LEDs). The benefit of automatic sound analysis is that our method can be used in any VR application without modifying the application itself. We evaluated the proposed method for 3D spatial sound visualization in a user study. Additionally, the conducted user study investigated which condition (corresponding to different senses) leads to faster performance in 3D sound localization task. For this purpose, we compared three conditions: haptic feedback only, LED feedback only, combined haptic and LED feedback. Our study results suggest that DHH participants could complete sound-related VR tasks significantly faster using LED and haptic+LED conditions in comparison to only haptic feedback. The presented method for spatial sound visualization can be directly used to enhance VR applications for use by DHH persons, and the results of our user study can serve as guidelines for the future design of accessible VR systems.

Information visualization techniques play an important role in Virtual Reality (VR) because they improve task performance, support cognitive processes, and eventually increase the feeling of immersion. Deaf and Hard-of-Hearing (DHH) persons have special needs for information presentation because they feel and perceive VR environments differently. Therefore, it is necessary to pay attention to requirements about presenting information in VR for this group of users. Previous research showed that adding special features and using haptic methods helps DHH persons to do VR tasks better. In this paper, we propose a novel Omni-directional particle visualization method and also evaluate multi-modal presentation methods in VR for DHH persons, such as audio, visual, haptic, and a combination of them (AVH). Additionally, we compare the results with the results of persons without hearing problems. The methods for information presentation in our study focus on spatial object localization in VR. Our user studies show that both DHH persons and persons without hearing problems were able to do VR tasks significantly faster using AVH. Also, we found out that DHH persons can do visual-related VR tasks faster than persons without hearing problems by using our new proposed visualization method. Our results suggest that the benefits of using audio among persons without hearing problems and the benefits of using vision among DHH persons cause an interesting balance in the results of AVH between both groups. Finally, our qualitative and quantitative evaluation indicates that both groups of participants preferred and enjoyed AVH modality more than other modalities.

Abstract To exploit the potential of immersive network analytics for engaging and effective exploration, we promote the metaphor of “Egocentrism”, where data depiction and interaction are adapted to the perspective of the user within a 3D network. Egocentrism has the potential to overcome some of the inherent downsides of virtual environments, e.g., visual clutter and cyber‐sickness. To investigate the effect of this metaphor on immersive network exploration, we designed and evaluated interfaces of varying degrees of Egocentrism. In a user study, we evaluated the effect of these interfaces on visual search tasks, efficiency of network traversal, spatial orientation, as well as cyber‐sickness. Results show that a simple Egocentric interface considerably improves visual search efficiency and navigation performance, yet does not decrease spatial orientation or increase cyber‐sickness. An occlusion‐free Ego‐Bubble view of the neighborhood only marginally improves the user's performance. We tie our findings together in an open online tool for Egocentric network exploration, providing actionable insights on the benefits of the Egocentric network exploration metaphor.

Virtual Reality (VR) has a great potential to improve skills of Deaf and Hard-of-Hearing (DHH) people. Most VR applications and devices are designed for persons without hearing problems. Therefore, DHH persons have many limitations when using VR. Adding special features in a VR environment, such as subtitles, or haptic devices will help them. Previously, it was necessary to design a special VR environment for DHH persons. We introduce and evaluate a new prototype called "EarVR" that can be mounted on any desktop or mobile VR Head-Mounted Display (HMD). EarVR analyzes 3D sounds in a VR environment and locates the direction of the sound source that is closest to a user. It notifies the user about the sound direction using two vibro-motors placed on the user's ears. EarVR helps DHH persons to complete sound-based VR tasks in any VR application with 3D audio and a mute option for background music. Therefore, DHH persons can use all VR applications with 3D audio, not only those applications designed for them. Our user study shows that DHH participants were able to complete a simple VR task significantly faster with EarVR than without. The completion time of DHH participants was very close to participants without hearing problems. Also, it shows that DHH participants were able to finish a complex VR task with EarVR, while without it, they could not finish the task even once. Finally, our qualitative and quantitative evaluation among DHH participants indicates that they preferred to use EarVR and it encouraged them to use VR technology more.

In this paper, we follow up on research dealing with motion learning in Virtual Reality (VR). We investigate the impact of VR motion learning on motion performance, motivation for motion learning and willingness to continue with the motion learning. In our research, we used three ball juggling as a subject of learning. We performed a user study with 30 participants. A VR application was used in our study which allows setting up lower gravity and thus slowing down the motion for learning purposes. The results were statistically evaluated and we comment on the positive influence of virtual reality on motivation and possibilities of using VR in the motion learning process.

This paper presents a novel method for illumination estimation from RGB-D images. The main focus of the proposed method is to enhance visual coherence in augmented reality applications by providing accurate and temporally coherent estimates of real illumination. For this purpose, we designed and trained a deep neural network which calculates a dominant light direction from a single RGB-D image. Additionally, we propose a novel method for real-time outlier detection to achieve temporally coherent estimates. Our method for light source estimation in augmented reality was evaluated on the set of real scenes. Our results demonstrate that the neural network can successfully estimate light sources even in scenes which were not seen by the network during training. Moreover, we compared our results with illumination estimates calculated by the state-of-the-art method for illumination estimation. Finally, we demonstrate the applicability of our method on numerous augmented reality scenes.

In this paper, we present a novel method for fast generation of furniture arrangements in interior scenes. Our method exploits the benefits of optimization-based approaches for global aesthetic rules and the advantages of procedural approaches for local arrangement of small objects. We generate the furniture arrangements for a given room in two steps: We first optimize the selection and arrangement of furniture objects in a room with respect to aesthetic and functional rules. The infinite trans-dimensional space of furniture layouts is rapidly explored by greedy cost minimization. In the second step, the procedural methods are locally applied in a stochastic fashion to generate important scene details. We demonstrate that our method achieves comparable results to a recent method for automatic interior design in terms of user preferences and that local procedural design enhances the result of optimization-based interior design. Additionally, our method is one order of magnitude faster than the compared method. Finally, the execution times of up to one second show that our method is suitable for generating large-scale indoor virtual environments during runtime.

In this paper, we present a system that automatically populates indoor virtual scenes with furniture objects and optimizes their positions and orientations with respect to aesthetic, ergonomic and functional rules called interior design guidelines. These guidelines are represented as mathematical expressions which form the cost function. Our system optimizes the set of multiple interior designs by minimizing the cost function using a genetic algorithm. Moreover, we extend the optimization to transdimensional space by enabling automatic selection of furniture objects. Finally, we optimize the assignment of materials to the furniture objects to achieve a unified design and harmonious color distribution. We investigate the capability of our system to generate sensible and livable interior designs in a perceptual study.

Fast and realistic synthesis of real videos with computer generated content has been a challenging problem in computer graphics. It involves computationally expensive light transport calculations. We present a novel and efficient algorithm for diffuse light transport calculation between virtual and real worlds called Differential Irradiance Caching. Our algorithm produces a high-quality result while preserving interactivity and allowing dynamic geometry, materials, lighting, and camera movement. The problem of expensive differential irradiance evaluation is solved by exploiting the spatial coherence in indirect illumination using irradiance caching. We enable multiple bounces of global illumination by using Monte Carlo integration in GPU ray-tracing to evaluate differential irradiance at irradiance cache records in one pass. The combination of ray-tracing and rasterization is used in an extended irradiance cache splatting algorithm to provide a fast GPU-based solution of indirect illumination. Limited information stored in the irradiance splat buffer causes errors for pixels on edges in case of depth of field rendering. We propose a solution to this problem using a reprojection technique to access the irradiance splat buffer. A novel cache miss detection technique is introduced which allows for a linear irradiance cache data structure. We demonstrate the integration of differential irradiance caching into a rendering framework for Mixed Reality applications capable of simulating complex global illumination effects.

In this paper we present a novel high-quality rendering system for Augmented Reality (AR). We study ray-tracing based rendering techniques in AR with the goal of achieving real-time performance and improving visual quality as well as visual coherence between real and virtual objects in a final composited image. A number of realistic and physically correct rendering effects are demonstrated, that have not been presented in real-time AR environments before. Examples are high-quality specular effects such as caustics, refraction, reflection, together with a depth of field effect and anti-aliasing. We present a new GPU implementation of photon mapping and its application for the calculation of caustics in environments where real and virtual objects are combined. The composited image is produced on-the-fly without the need of any preprocessing step. A main contribution of our work is the achievement of interactive rendering speed for high-quality ray-tracing algorithms in AR setups. Finally we performed an evaluation to study how users perceive visual quality and visual coherence with different realistic rendering effects. The results of our user study show that in 40.1% cases users mistakenly judged virtual objects as real ones. Moreover we show that high-quality rendering positively affects the perceived visual coherence.