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 data set, allowing us to assess GTOM techniques in a set of 30 particularly challenging cases.

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 data set, 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. While a huge body of softshadow 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 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, e.g., 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.

User MappingsPredicted Point Density Fig. 1. We investigate how people map points between dissimilar shapes (left) and show that our findings can be used to define statistical models that allow us to transfer individual objects, like trees (middle), and/or procedural content, like ponds, forests and flowers (right), between dissimilar shapes.Abstract-Finding similar points in globally or locally similar shapes has been studied extensively through the use of various point descriptors or shape-matching methods. However, little work exists on finding similar points in dissimilar shapes. In this paper, we present the results of a study where users were given two dissimilar two-dimensional shapes and asked to map a given point in the first shape to the point in the second shape they consider most similar. We find that user mappings in this study correlate strongly with simple geometric relationships between points and shapes. To predict the probability distribution of user mappings between any pair of simple two-dimensional shapes, two distinct statistical models are defined using these relationships. We perform a thorough validation of the accuracy of these predictions and compare our models qualitatively and quantitatively to well-known shape-matching methods. Using our predictive models, we propose an approach to map objects or procedural content between different shapes in different design scenarios.