ABSTRACT
Head tracking is commonly used in VR applications to allow users to naturally view 3D content using physical head movement, but many applications also support turning with hand-held controllers. Controller and joystick controls are convenient for practical settings where full 360-degree physical rotation is not possible, such as when the user is sitting at a desk. Though controller-based rotation provides the benefit of convenience, previous research has demonstrated that virtual or joystick-controlled view rotation to have drawbacks of sickness and disorientation compared to physical turning. To combat such issues, researchers have considered various techniques such as speed adjustments or reduced field of view, but data is limited on how different variations for joystick rotation influences sickness and orientation perception. Our studies include different variations of techniques such as joystick rotation, resetting, and field-of-view reduction. We investigate trade-offs among different techniques in terms of sickness and the ability to maintain spatial orientation. In two controlled experiments, participants traveled through a sequence of rooms and were tested on spatial orientation, and we also collected subjective measures of sickness and preference. Our findings indicate a preference by users towards directly-manipulated joystick-based rotations compared to user-initiated resetting and minimal effects of technique on spatial awareness.
ABSTRACT
In many applications, developed deep-learning models need to be iteratively debugged and refined to improve the model efficiency over time. Debugging some models, such as temporal multilabel classification (TMLC) where each data point can simultaneously belong to multiple classes, can be especially more challenging due to the complexity of the analysis and instances that need to be reviewed. In this article, focusing on video activity recognition as an application of TMLC, we propose DETOXER, an interactive visual debugging system to support finding different error types and scopes through providing multiscope explanations.
ABSTRACT
The visualization of hierarchically structured data over time is an ongoing challenge and several approaches exist trying to solve it. Techniques such as animated or juxtaposed tree visualizations are not capable of providing a good overview of the time series and lack expressiveness in conveying changes over time. Nested streamgraphs provide a better understanding of the data evolution, but lack the clear outline of hierarchical structures at a given timestep. Furthermore, these approaches are often limited to static hierarchies or exclude complex hierarchical changes in the data, limiting their use cases. We propose a novel visual metaphor capable of providing a static overview of all hierarchical changes over time, as well as clearly outlining the hierarchical structure at each individual time step. Our method allows for smooth transitions between treemaps and nested streamgraphs, enabling the exploration of the trade-off between dynamic behavior and hierarchical structure. As our technique handles topological changes of all types, it is suitable for a wide range of applications. We demonstrate the utility of our method on several use cases, evaluate it with a user study, and provide its full source code.
