In this paper we present AffordIt!, a tool for adding affordances to the component parts of a virtual object. Following 3D scene reconstruction and segmentation procedures, users find themselves with complete virtual objects, but no intrinsic behaviors have been assigned, forcing them to use unfamiliar Desktop-based 3D editing tools. AffordIt! offers an intuitive solution that allows a user to select a region of interest for the mesh cutter tool, assign an intrinsic behavior and view an animation preview of their work. To evaluate the usability and workload of AffordIt! we ran an exploratory study to gather feedback. In the study we utilize two mesh cutter shapes that select a region of interest and two movement behaviors that a user then assigns to a common household object. The results show high usability with low workload ratings, demonstrating the feasibility of AffordIt! as a valuable 3D authoring tool. Based on these initial results we also present a road-map of future work that will improve the tool in future iterations.

Accurate tracking of a user in a marker-less environment can be difficult, even more so when agile head or hand movements are expected. When relying on feature detection as part of a SLAM algorithm the issue arises that a large rotational delta causes previously tracked features to become lost. One approach to overcome this problem is with multiple sensors increasing the horizontal field of view. In this paper, we perform a systematic evaluation of tracking accuracy by recording several agile movements and providing different camera configurations to evaluate against. We begin with four sensors in a square configuration and test the resulting output from a chosen SLAM algorithm. We then systematically remove a camera from the feed covering all permutations to determine the level of accuracy and tracking loss. We cover some of the lessons learned in this preliminary experiment and how it may guide researchers in tracking extremely agile movements.

GestureBar is a novel, approachable UI for learning gestural interactions that enables a walk-up-and-use experience which is in the same class as standard menu and toolbar interfaces. GestureBar leverages the familiar, clean look of a common toolbar, but in place of executing commands, richly discloses how to execute commands with gestures, through animated images, detail tips and an out-of-document practice area. GestureBar's simple design is also general enough for use with any recognition technique and for integration with standard, non-gestural UI components. We evaluate GestureBar in a formal experiment showing that users can perform complex, ecologically valid tasks in a purely gestural system without training, introduction, or prior gesture experience when using GestureBar, discovering and learning a high percentage of the gestures needed to perform the tasks optimally, and significantly outperforming a state of the art crib sheet. The relative contribution of the major design elements of GestureBar is also explored. A second experiment shows that GestureBar is preferred to a basic crib sheet and two enhanced crib sheet variations.