Abstract Dental healthcare increasingly employs computer‐aided design software, to provide patients with high‐quality dental prosthetic devices. In modern dental reconstruction, dental technicians address the unique anatomy of each patient individually, by capturing the dental impression and measuring the mandibular movements. Subsequently, dental technicians design a custom denture that fits the patient from a functional point of view. The current Workflow does not include a systematic analysis of aesthetics, and dental technicians rely only on an aesthetically pleasing mock‐up that they discuss with the patient, and on their experience. Therefore, the final denture aesthetics remain unknown until the dental technicians incorporate the denture into the patient. In this Work, we present a solution that integrates aesthetics analysis into the functional Workflow of dental technicians. Our solution uses a video recording of the patient, to preview the denture design at any stage of the denture design process. We present a teeth pose estimation technique that enables denture preview and a set of linked visualizations that support dental technicians in the aesthetic design of dentures. These visualizations assist dental technicians in choosing the most aesthetically fitting preset from a library of dentures, in identifying the suitable denture size, and in adjusting the denture position. We demonstrate the utility of our system with four use cases, explored by a dental technician. Also, we performed a quantitative evaluation for teeth pose estimation, and an informal usability evaluation, with positive outcomes concerning the integration of aesthetics analysis into the functional Workflow.

With the emergence of affordable 3D displays, stereoscopy is becoming a commodity. However, often users report discomfort even after brief exposures to stereo content. One of the main reasons is the conflict between vergence and accommodation that is caused by 3D displays. We investigate dynamic adjustment of stereo parameters in a scene using gaze data in order to reduce discomfort. In a user study, we measured stereo fusion times after abrupt manipulation of disparities using gaze data. We found that gaze-controlled manipulation of disparities can lower fusion times for large disparities. In addition we found that gaze-controlled disparity adjustment should be applied in a personalized manner and ideally performed only at the extremities or outside the comfort zone of subjects. These results provide important insight on the problems associated with fast disparity manipulation and are essential for developing appealing gaze-contingent and gaze-controlled applications.

Stimuli obtained from highly dynamic 3D virtual environments and synchronous eye-tracking data are commonly used by algorithms that strive to correlate gaze to scene objects, a process referred to as gaze-to-object mapping (GTOM) . We propose to address this problem with a probabilistic approach using Bayesian inference. The desired result of the inference is a predicted probability density function (PDF) specifying for each object in the scene a probability to be attended by the user. To evaluate the quality of a predicted attention PDF, we present a methodology to assess the information value (i.e., likelihood) in the predictions of different approaches that can be used to infer object attention. To this end, we propose an experiment based on a visual search task, which allows us to determine the object of attention at a certain point in time under controlled conditions. We perform this experiment with a wide range of static and dynamic visual scenes to obtain a ground-truth evaluation dataset, allowing us to assess GTOM techniques in a set of 30 particularly challenging cases.

We performed a perceptual user study of algorithms that approximate soft shadows in real time. Although a huge body of soft-shadow algorithms have been proposed, to our knowledge this is the first methodical study for comparing different real-time shadow algorithms with respect to their plausibility and visual appearance. We evaluated soft-shadow properties like penumbra overlap with respect to their relevance to shadow perception in a systematic way, and we believe that our results can be useful to guide future shadow approaches in their methods of evaluation. In this study, we also capture the predominant case of an inexperienced user observing shadows without comparing to a reference solution, such as when watching a movie or playing a game. One important result of this experiment is to scientifically verify that real-time soft-shadow algorithms, despite having become physically based and very realistic, can nevertheless be intuitively distinguished from a correct solution by untrained users.

Focus+context techniques provide visual guidance in visualizations by giving strong visual prominence to elements of interest while the context is suppressed. However, finding a visual feature to enhance for the focus to pop out from its context in a large dynamic scene, while leading to minimal visual deformation and subjective disturbance, is challenging. This paper proposes Attractive Flicker, a novel technique for visual guidance in dynamic narrative visualizations. We first show that flicker is a strong visual attractor in the entire visual field, without distorting, suppressing, or adding any scene elements. The novel aspect of our Attractive Flicker technique is that it consists of two signal stages: The first "orientation stage" is a short but intensive flicker stimulus to attract the attention to elements of interest. Subsequently, the intensive flicker is reduced to a minimally disturbing luminance oscillation ("engagement stage") as visual support to keep track of the focus elements. To find a good trade-off between attraction effectiveness and subjective annoyance caused by flicker, we conducted two perceptual studies to find suitable signal parameters. We showcase Attractive Flicker with the parameters obtained from the perceptual statistics in a study of molecular interactions. With Attractive Flicker, users were able to easily follow the narrative of the visualization on a large display, while the flickering of focus elements was not disturbing when observing the context.

Audio rendering is generally used to increase the realism of virtual environments (VE). In addition, audio rendering may also improve the performance in specific tasks carried out in interactive applications such as games or simulators. In this article we investigate the effect of the quality of sound rendering on task performance in a task which is inherently vision-dominated. The task is a virtual traffic gap-crossing scenario with two elements: first, to discriminate crossable and uncrossable gaps in oncoming traffic, and second, to find the right timing to start crossing the street without an accident. A study was carried out with 48 participants in an immersive virtual environment setup with a large screen and headphones. Participants were grouped into three different scenarios. In the first one, spatialized audio rendering with head-related transfer function (HRTF) filtering was used. The second group was tested with conventional stereo rendering, and the remaining group ran the experiment in a mute condition. Our results give a clear evidence that spatialized audio improves task performance compared to the unimodal mute condition. Since all task-relevant information was in the participants' field-of-view, we conclude that an enhancement of task performance results from a bimodal advantage due to the integration of visual and auditory spatial cues.

Gaze analysis and prediction in interactive virtual environments, such as games, is a challenging topic since the 3D perspective and variations of the viewpoint as well as the current task introduce many variables that affect the distribution of gaze. In this article, we present a novel pipeline to study eye-tracking data acquired from interactive 3D applications. The result of the pipeline is an importance map which scores the amount of gaze spent on each object. This importance map is then used as a heuristic to predict a user's visual attention according to the object properties present at runtime. The novelty of this approach is that the analysis is performed in object space and the importance map is defined in the feature space of high-level properties. High-level properties are used to encode task relevance and other attributes, such as eccentricity, which may have an impact on gaze behavior. The pipeline has been tested with an exemplary study on a first-person shooter game. In particular, a protocol is presented describing the data acquisition procedure, the learning of different importance maps from the data, and finally an evaluation of the performance of the derived gaze predictors. A metric measuring the degree of correlation between attention predicted by the importance map and the actual gaze yielded clearly positive results. The correlation becomes particularly strong when the player is attentive to an in-game task.