We present a framework for the analysis and evaluation of travel, or viewpoint motion control, techniques for use in immersive virtual environments (VEs). In previous work, we presented a taxonomy of travel techniques and a set of experiments mapping parts of the taxonomy to various performance metrics. Since these initial experiments, we have expanded the framework to allow evaluation of not only the effects of different travel techniques, but also the effects of many outside factors simultaneously. Combining this expanded framework with the measurement of multiple response variables epitomises the philosophy of testbed evaluation. This experimental philosophy leads to a deeper understanding of the interaction and the technique(s) in question, as well as to broadly generalisable results. We also present an example experiment within this expanded framework, which evaluates the user's ability to gather information while travelling through a virtual environment. Results indicate that, of the variables tested, the complexity of the environment is by far the most important factor.

Advances in a variety of computing fields, including "big data," machine learning, visualization, and augmented/mixed/virtual reality, have combined to give rise to the emerging field of immersive analytics, which investigates how these new technologies support analysis and decision making. Thus far, we feel that immersive analytics research has been somewhat ad hoc, possibly owing to the fact that there is not yet an organizing framework for immersive analytics research. In this paper, we address this lack by proposing a definition for immersive analytics and identifying some general research areas and specific research questions that will be important for the development of this field. We also present three case studies that, while all being examples of what we would consider immersive analytics, present different challenges, and opportunities. These serve to demonstrate the breadth of immersive analytics and illustrate how the framework proposed in this paper applies to practical research.

The Virtual Reality Gorilla Exhibit teaches users about gorilla behaviors and social interactions. We present techniques for building the environment and the virtual gorillas that inhabit it. .

This paper presents Force Push, a novel gesture-based interaction technique for remote object manipulation in virtual reality (VR). Inspired by the design of magic powers in popular culture, Force Push uses intuitive hand gestures to drive physics-based movement of the object. Using a novel algorithm that dynamically maps rich features of hand gestures to the properties of the physics simulation, both coarse-grained ballistic movements and fine-grained refinement movements can be achieved seamlessly and naturally. An initial user study of a limited translation task showed that, although its gesture-to-force mapping is inherently harder to control than traditional position-to-position mappings, Force Push is usable even for extremely difficult tasks. Direct position-to-position control outperformed Force Push when the initial distance between the object and the target was close relative to the required accuracy; however, the gesture-based method began to show promising results when they were far away from each other. As for subjective user experience, Force Push was perceived as more natural and fun to use, even though its controllability and accuracy were thought to be inferior to direct control. This paper expands the design space of object manipulation beyond mimicking reality, and provides hints on using magical gestures and physics-based techniques for higher usability and hedonic qualities in user experience.

The fundamental question for an information-rich virtual environment is how to access and display abstract information. We investigated two existing navigation techniques:Hand-Centered Object Manipulation Extending Ray-casting (HOMER) and Go-Go navigation, and two text layout techniques: within-the-world display (WWD) and heads-up display (HUD). Four search tasks were performed to measure participants' performance in a densely packed environment. HUD enabled significantly better performance than WWD and the Go-Go technique enabled better performance than the HOMER technique for most of the tasks. We found that using HOMER navigation combined with the WWD technique was significantly worse than other combinations for difficult naïve search tasks. Users also preferred the combination of Go-Go and HUD for all tasks.