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Voronoi diagrams and their computation are well known in the Euclidean L2 space. They are easy to sample and render in generalized Lp spaces but nontrivial to construct geometrically. Especially the limit of this norm with p → ∞ lends itself to many quad‐ and hex‐meshing related applications as the level‐set in this space is a hypercube. Many application scenarios circumvent the actual computation of L∞ diagrams altogether as known concepts for these diagrams are limited to 2D, uniformly weighted and axis‐aligned sites. Our novel algorithm allows for the construction of generalized L∞ Voronoi diagrams. Although parts of the developed concept theoretically extend to higher dimensions it is herein presented and evaluated for the 2D and 3D case. It further supports individually oriented sites and allows for generating weighted diagrams with anisotropic weight vectors for individual sites. The algorithm is designed around individual sites, and initializes their cells with a simple meshed representation of a site's level‐set. Hyperplanes between adjacent cells cut the initialization geometry into convex polyhedra. Non‐cell geometry is filtered out based on the L∞ Voronoi criterion, leaving only the non‐convex cell geometry. Eventually we conclude with discussions on the algorithms complexity, numerical precision and analyze the applicability of our generalized L∞ diagrams for the construction of Centroidal Voronoi Tessellations (CVT) using Lloyd's algorithm. [ABSTRACT FROM AUTHOR]

Our work investigates the influence of self-related cues in the design of virtual humans on body perception in virtual reality. In a $2\times 2$ mixed design, 64 participants faced photorealistic virtual humans either as a motion-synchronized embodied avatar or as an autonomous moving agent, appearing subsequently with a personalized and generic texture. Our results unveil that self-related cues through embodiment and personalization yield an individual and complemented increase in participants' sense of embodiment and self-identification towards the virtual human. Different body weight modification and estimation tasks further showed an impact of both factors on participants' body weight perception. Additional analyses revealed that the participant's body mass index predicted body weight estimations in all conditions and that participants' self-esteem and body shape concerns correlated with different body weight perception results. Hence, we have demonstrated the occurrence of double standards through induced self-related cues in virtual human perception, especially through embodiment.

People living with dementia (PLWD) may experience the episodes of lucidity (ELs), defined as a sudden return of abilities presumed to have been lost and presenting as meaningful communication and connection. Early research on ELs in advanced disease stages suggests these are predominantly positive events. This case report draws from 1 outlier case from a descriptive qualitative study on caregivers of PLWDs' experiences with ELs. The caregiver-reported events perceived as an EL being accompanied with unexpected self-awareness, leading to a negative emotional reaction and feelings of loss. This case report contributes insights to the current understandings of ELs as exclusively relevant in advanced stages of dementia, as similar events may present in earlier disease stages, and may be associated with the negative emotional experiences among PLWD. Future research is needed to consider the ethical implications surrounding ELs and to support the caregivers in anticipating and responding to ELs., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Visual landmarks provide crucial information for human navigation. But what characteristics define a landmark? To be uniquely recognized, a landmark should be distinctive and salient, while providing precise and accurate positional information. It should also be permanent. For example, to find back to your car, a nearby church seems a better landmark compared with a distinct truck or bicycle, because you learned that there is a chance that these objects might move. To this end, we investigated human learning of landmark permanency for navigation while treating spatiotemporal permanency as a probabilistic property. We hypothesized that humans will be able to learn the probabilistic nature of landmark permanency and assign higher weight to more permanent landmarks. To test this hypothesis, we designed a homing task where participants had to return to a position that was surrounded by three landmarks. In the learning phase we manipulated the spatiotemporal permanency of one landmark by secretly repositioning it before participants returned home. In the test phase, we investigated the weight allocated to the nonpermanent landmark by analyzing its influence on the navigational performance during homing. We conducted four experiments: In the first two experiments we altered the statistics of permanency and accordingly found an influence on participants' behavior, nonpermanent objects were used less for finding home. In the last two experiments we investigated the role of short-term learning of novel statistics versus long-term knowledge about such statistics. No carry-over effects in Experiment 3 and very little influence of object identity with different long-term permanency characteristics in Experiment 4 revealed a dominance of short-term learning over the use of long-term a priori knowledge about object permanency. This indicates that long-term prior beliefs are quickly updated by the current permanency statistics. Taken together, consistent with a Bayesian account for navigation these results indicate that humans quickly learn and update the statistics of landmark permanency and use it in an effective way, assigning gradually more weight to the more permanent landmark and making it more important for navigation. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Background and Objectives: Episodes of lucidity (ELs), or a transient return of abilities believed to be lost in people living with dementia, are a growing area of interest. These events hold important implications for care, caregiving, and our understanding of underlying etiologies. Research on ELs is largely limited to retrospective reports. The perspectives of professional and family caregivers on ELs and research approaches can inform efforts to define and study lucidity. The present study examined family caregiver and hospice clinician experiences with and perspectives on ELs in people living with dementia and observational approaches to studying these events., Research Design and Methods: This exploratory, descriptive qualitative study employed semistructured interviews (N = 20 caregivers, N = 6 clinicians). Data were analyzed using Rapid Identification of Themes and subsequent duplicate review of interview data to enhance trustworthiness., Results: Most participants readily recalled events they perceived as ELs, describing a transient return of abilities they felt was significant and/or meaningful. Defining features, interpretations, and the perceived impact of ELs varied, although ELs were commonly conceptualized as a manifestation of self. Caregivers described extensive efforts to detect patterns and supportive social conditions for ELs. Participants supported use of audiovisual observation to study ELs and provided recommendations for privacy, workflow, and caregiver engagement., Discussion and Implications: Interpretations of ELs are heterogeneous, and recognition of these events may necessitate close familiarity with the person living with dementia. Participants endorse observational approaches and integration of caregivers in this research., (© The Author(s) 2022. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigation Systems (INSs) that obtain position, velocity, and Course Over Ground (COG) correction data from Satellite Navigation (SatNav). However, SatNav is not always available, as is the case of roofed places, such as parking structures, tunnels, or urban canyons. This leads to a degradation over time of the estimated vehicle state. In the present paper, a methodology is proposed that consists on the use of a Machine Learning (ML)-method (Transformer Neural Network—TNN) with the objective of generating highly accurate velocity correction data from On-Board Diagnostics (OBD) data. The TNN obtains OBD data as input and measurements from state-of-the-art reference sensors as a learning target. The results show that the TNN is able to infer the velocity over ground with a Mean Absolute Error (MAE) of 0.167 kmh (0.046 ms) when a database of 3,428,099 OBD measurements is considered. The accuracy decreases to 0.863 kmh (0.24 ms) when only 5000 OBD measurements are used. Given that the obtained accuracy closely resembles that of state-of-the-art reference sensors, it allows INSs to be provided with accurate velocity correction data. An inference time of less than 40 ms for the generation of new correction data is achieved, which suggests the possibility of online implementation. This supports a highly accurate estimation of the vehicle state for the evaluation and validation of AD and ADAS, even in SatNav-deprived environments.

The prediction of the motion of traffic participants is a crucial aspect for the research and development of Automated Driving Systems (ADSs). Recent approaches are based on multi-modal motion prediction, which requires the assignment of a probability score to each of the multiple predicted motion hypotheses. However, there is a lack of ground truth for this probability score in the existing datasets. This implies that current Machine Learning (ML) models evaluate the multiple predictions by comparing them with the single real trajectory labeled in the dataset. In this work, a novel data-based method named Probabilistic Traffic Motion Labeling (PROMOTING) is introduced in order to (a) generate probable future routes and (b) estimate their probabilities. PROMOTING is presented with the focus on urban intersections. The generation of probable future routes is (a) based on a real traffic dataset and consists of two steps: first, a clustering of intersections with similar road topology, and second, a clustering of similar routes that are driven in each cluster from the first step. The estimation of the route probabilities is (b) based on a frequentist approach that considers how traffic participants will move in the future given their motion history. PROMOTING is evaluated with the publicly available Lyft database. The results show that PROMOTING is an appropriate approach to estimate the probabilities of the future motion of traffic participants in urban intersections. In this regard, PROMOTING can be used as a labeling approach for the generation of a labeled dataset that provides a probability score for probable future routes. Such a labeled dataset currently does not exist and would be highly valuable for ML approaches with the task of multi-modal motion prediction. The code is made open source.

Motion capture of unrestrained moving animals is a major analytic tool in neuroethology and behavioral physiology. At present, several motion capture methodologies have been developed, all of which have particular limitations regarding experimental application. Whereas marker-based motion capture systems are very robust and easily adjusted to suit different setups, tracked species, or body parts, they cannot be applied in experimental situations where markers obstruct the natural behavior (e.g., when tracking delicate, elastic, and/or sensitive body structures). On the other hand, marker-less motion capture systems typically require setup- and animal-specific adjustments, for example by means of tailored image processing, decision heuristics, and/or machine learning of specific sample data. Among the latter, deep-learning approaches have become very popular because of their applicability to virtually any sample of video data. Nevertheless, concise evaluation of their training requirements has rarely been done, particularly with regard to the transfer of trained networks from one application to another. To address this issue, the present study uses insect locomotion as a showcase example for systematic evaluation of variation and augmentation of the training data. For that, we use artificially generated video sequences with known combinations of observed, real animal postures and randomized body position, orientation, and size. Moreover, we evaluate the generalization ability of networks that have been pre-trained on synthetic videos to video recordings of real walking insects, and estimate the benefit in terms of reduced requirement for manual annotation. We show that tracking performance is affected only little by scaling factors ranging from 0.5 to 1.5. As expected from convolutional networks, the translation of the animal has no effect. On the other hand, we show that sufficient variation of rotation in the training data is essential for performance, and make concise suggestions about how much variation is required. Our results on transfer from synthetic to real videos show that pre-training reduces the amount of necessary manual annotation by about 50%., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Arent, Schmidt, Botsch and Dürr.)

A current trend in automotive research is autonomous driving. For the proper testing and validation of automated driving functions a reference vehicle state is required. Global Navigation Satellite Systems (GNSS) are useful in the automation of the vehicles because of their practicality and accuracy. However, there are situations where the satellite signal is absent or unusable. This research work presents a methodology that addresses those situations, thus largely reducing the dependency of Inertial Navigation Systems (INSs) on the SatNav. The proposed methodology includes (1) a standstill recognition based on machine learning, (2) a detailed mathematical description of the horizontation of inertial measurements, (3) sensor fusion by means of statistical filtering, (4) an outlier detection for correction data, (5) a drift detector, and (6) a novel LiDAR-based Positioning Method (LbPM) for indoor navigation. The robustness and accuracy of the methodology are validated with a state-of-the-art INS with Real-Time Kinematic (RTK) correction data. The results obtained show a great improvement in the accuracy of vehicle state estimation under adverse driving conditions, such as when the correction data is corrupted, when there are extended periods with no correction data and in the case of drifting. The proposed LbPM method achieves an accuracy closely resembling that of a system with RTK.

Objectives: Visual field defects due to hemi- or quadrantanopia after stroke represent an under-recognized neurological symptom with inefficient instruments for neurorehabilitation to date. We here examined the effects of training in a virtual reality (VR) supermarket on cognitive functions, depressive symptoms, and subjective cognitive complaints in patients with hemianopia/quadrantanopia and healthy controls., Methods: During a 14-day rehabilitation program, 20 patients and 20 healthy controls accomplished a real-life-like shopping task in a VR supermarket. A comparison between pre- and post-training standard neuropsychological measures, depressive symptoms, and subjective memory complaints allowed us to assess a putative transfer of rehabilitation effects from the training tasks to specific cognitive functions., Results: The results indicate that VR training may improve performance not only in the trained task but also in specific neuropsychological functions. After the training, both patients and controls showed improved performances in visual scanning, mental rotation, visuoconstruction, and cognitive flexibility. Moreover, depressive symptoms were attenuated in both groups. In the patient group compared to the control group, the training particularly resulted in improved visual memory retrieval and reduced memory complaints., Conclusions: The results of the current study suggest that VR training can improve particularly visual-spatial skills in patients with hemianopia or quadrantanopia. Our study thus introduces an interesting novel treatment approach to improve cognitive functions relevant to daily life in stroke patients with visual field defects.

Feedback is essential for skill acquisition as it helps identifying and correcting performance errors. Nowadays, Virtual Reality can be used as a tool to guide motor learning, and to provide innovative types of augmented feedback that exceed real world opportunities. Concurrent feedback has shown to be especially beneficial for novices. Moreover, watching skilled performances helps novices to acquire a motor skill, and this effect depends on the perspective taken by the observer. To date, however, the impact of watching one's own performance together with full body superimposition of a skilled performance, either from the front or from the side, remains to be explored. Here we used an immersive, state-of-the-art, low-latency cave automatic virtual environment (CAVE), and we asked novices to perform squat movements in front of a virtual mirror. Participants were assigned to one of three concurrent visual feedback groups: participants either watched their own avatar performing full body movements or were presented with the movement of a skilled individual superimposed on their own performance during movement execution, either from a frontal or from a side view. Motor performance and cognitive representation were measured in order to track changes in movement quality as well as motor memory across time. Consistent with our hypotheses, results showed an advantage of the groups that observed their own avatar performing the squat together with the superimposed skilled performance for some of the investigated parameters, depending on perspective. Specifically, for the deepest point of the squat, participants watching the squat from the front adapted their height, while those watching from the side adapted their backward movement. In a control experiment, we ruled out the possibility that the observed improvements were due to the mere fact of performing the squat movements-irrespective of the type of visual feedback. The present findings indicate that it can be beneficial for novices to watch themselves together with a skilled performance during execution, and that improvement depends on the perspective chosen., (Copyright © 2019 Hülsmann, Frank, Senna, Ernst, Schack and Botsch.)

This article investigates performance and user experience in Social Virtual Reality (SVR) targeting distributed, embodied, and immersive, face-to-face encounters. We demonstrate the close relationship between scalability, reproduction accuracy, and the resulting performance characteristics, as well as the impact of these characteristics on users co-located with larger groups of embodied virtual others. System scalability provides a variable number of co-located avatars and Al-controlled agents with a variety of different appearances, including realistic-looking virtual humans generated from photogrammetry scans. The article reports on how to meet the requirements of embodied SVR with today's technical off-the-shelf solutions and what to expect regarding features, performance, and potential limitations. Special care has been taken to achieve low latencies and sufficient frame rates necessary for reliable communication of embodied social signals. We propose a hybrid evaluation approach which coherently relates results from technical benchmarks to subjective ratings and which confirms required performance characteristics for the target scenario of larger distributed groups. A user-study reveals positive effects of an increasing number of co-located social companions on the quality of experience of virtual worlds, i.e., on presence, possibility of interaction, and co-presence. It also shows that variety in avatar/agent appearance might increase eeriness but might also stimulate an increased interest of participants about the environment.

Objective: Neuropsychological patients often suffer from impairments in visual selective attention and processing capacity components. Their assessment demands a high standardization of testing conditions, which is difficult to achieve across institutions. Head-mounted displays (HMDs) provide a solution. These virtual reality devices cover the entire visual field in a shielded way and thus keep visual stimulation constant. For neuropsychological assessment with HMDs, sufficient reliability is required. We have previously demonstrated that an early developer version of an HMD can be used to reliably measure components of visual processing capacity. However, it is unclear whether this also holds for the assessment of components of visual selective attention. Moreover, it has yet to be established whether now commercially available HMDs are capable of reliable neuropsychological assessment., Method: We assessed the test-retest reliabilities of several components of visual selective attention and processing capacity of healthy subjects with the commercially available HTC Vive. Using an assessment procedure (combiTVA) derived from the theory of visual attention (TVA; Bundesen, 1990), we measured attentional selectivity, lateral bias, processing speed, visual working memory capacity, and the threshold of conscious perception. We compared the reliabilities of these components measured with the HTC Vive with those of a cathode ray tube (CRT) screen, the gold standard of visual presentation in the laboratory., Results: Both devices provided comparable reliabilities., Conclusions: Thus, HMDs fulfill the requirement to replace standard screens. With their inherent visual standardization and portability, they offer unprecedented opportunities for neuropsychological assessment, such as computerized bedside testing and comparisons of test values across institutions. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

In this paper, we present a method for automated estimation of a human face given a skull remain. Our proposed method is based on three statistical models. A volumetric (tetrahedral) skull model encoding the variations of different skulls, a surface head model encoding the head variations, and a dense statistic of facial soft tissue thickness (FSTT). All data are automatically derived from computed tomography (CT) head scans and optical face scans. In order to obtain a proper dense FSTT statistic, we register a skull model to each skull extracted from a CT scan and determine the FSTT value for each vertex of the skull model towards the associated extracted skin surface. The FSTT values at predefined landmarks from our statistic are well in agreement with data from the literature. To recover a face from a skull remain, we first fit our skull model to the given skull. Next, we generate spheres with radius of the respective FSTT value obtained from our statistic at each vertex of the registered skull. Finally, we fit a head model to the union of all spheres. The proposed automated method enables a probabilistic face-estimation that facilitates forensic recovery even from incomplete skull remains. The FSTT statistic allows the generation of plausible head variants, which can be adjusted intuitively using principal component analysis. We validate our face recovery process using an anonymized head CT scan. The estimation generated from the given skull visually compares well with the skin surface extracted from the CT scan itself., Competing Interests: The authors have declared that no competing interests exist.

This article reports the impact of the degree of personalization and individualization of users' avatars as well as the impact of the degree of immersion on typical psychophysical factors in embodied Virtual Environments. We investigated if and how virtual body ownership (including agency), presence, and emotional response are influenced depending on the specific look of users' avatars, which varied between (1) a generic hand-modeled version, (2) a generic scanned version, and (3) an individualized scanned version. The latter two were created using a state-of-the-art photogrammetry method providing a fast 3D-scan and post-process workflow. Users encountered their avatars in a virtual mirror metaphor using two VR setups that provided a varying degree of immersion, (a) a large screen surround projection (L-shape part of a CAVE) and (b) a head-mounted display (HMD). We found several significant as well as a number of notable effects. First, personalized avatars significantly increase body ownership, presence, and dominance compared to their generic counterparts, even if the latter were generated by the same photogrammetry process and hence could be valued as equal in terms of the degree of realism and graphical quality. Second, the degree of immersion significantly increases the body ownership, agency, as well as the feeling of presence. These results substantiate the value of personalized avatars resembling users' real-world appearances as well as the value of the deployed scanning process to generate avatars for VR-setups where the effect strength might be substantial, e.g., in social Virtual Reality (VR) or in medical VR-based therapies relying on embodied interfaces. Additionally, our results also strengthen the value of fully immersive setups which, today, are accessible for a variety of applications due to the widely available consumer HMDs.

Vision unfolds as an intricate pattern of information processing over time. Studying vision and visual cognition therefore requires precise manipulations of the timing of visual stimulus presentation. Although standard computer display technologies offer great accuracy and precision of visual presentation, their temporal resolution is limited. This limitation stems from the fact that the presentation of rendered stimuli has to wait until the next refresh of the computer screen. We present a novel method for presenting visual stimuli with ultrahigh temporal resolution (<1 ms) on newly available gaming monitors. The method capitalizes on the G-Sync technology, which allows for presenting stimuli as soon as they have been rendered by the computer's graphics card, without having to wait for the next screen refresh. We provide software implementations in the three programming languages C++, Python (using PsychoPy2), and Matlab (using Psychtoolbox3). For all implementations, we confirmed the ultrahigh temporal resolution of visual presentation with external measurements by using a photodiode. Moreover, a psychophysical experiment revealed that the ultrahigh temporal resolution impacts on human visual performance. Specifically, observers' object recognition performance improved over fine-grained increases of object presentation duration in a theoretically predicted way. Taken together, the present study shows that the G-Sync-based presentation method enables researchers to investigate visual processes whose data patterns were concealed by the low temporal resolution of previous technologies. Therefore, this new presentation method may be a valuable tool for experimental psychologists and neuroscientists studying vision and its temporal characteristics.

Cartoon characters are omnipresent in popular media. While few studies have scientifically investigated their processing, in computer graphics, efforts are made to increase realism. Yet, close approximations of reality have been suggested to evoke sometimes a feeling of eeriness, the "uncanny valley" effect. Here, we used high-density electroencephalography to investigate brain responses to professionally stylized happy, angry, and neutral character faces. We employed six face-stylization levels varying from abstract to realistic and investigated the N170, early posterior negativity (EPN), and late positive potential (LPP) event-related components. The face-specific N170 showed a u-shaped modulation, with stronger reactions towards both most abstract and most realistic compared to medium-stylized faces. For abstract faces, N170 was generated more occipitally than for real faces, implying stronger reliance on structural processing. Although emotional faces elicited highest amplitudes on both N170 and EPN, on the N170 realism and expression interacted. Finally, LPP increased linearly with face realism, reflecting activity increase in visual and parietal cortex for more realistic faces. Results reveal differential effects of face stylization on distinct face processing stages and suggest a perceptual basis to the uncanny valley hypothesis. They are discussed in relation to face perception, media design, and computer graphics.