We investigate how path context, encompassing both comfort and attractiveness, shapes user experiences in outdoor locative storytelling using Augmented Reality (AR). Addressing a research gap that predominantly concentrates on indoor settings or narrative backdrops, our user-focused research delves into the interplay between perceived path context and locative AR storytelling on routes with diverse walkability levels. We examine the correlation and causation between narrative engagement, spatial presence, perceived workload, and perceived path context. Our findings show that on paths with reasonable path walkability, attractive elements positively influence the narrative experience. However, even in environments with assured narrative walkability, inappropriate safety elements can divert user attention to mobility, hindering the integration of real-world features into the narrative. These results carry significant implications for path creation in outdoor locative AR storytelling, underscoring the importance of ensuring comfort and maintaining a balance between comfort and attractiveness to enrich the outdoor AR storytelling experience.

This paper proposes a novel panoramic texture mapping-based rendering system for real-time, photorealistic reproduction of large-scale urban scenes at a street level. Various image-based rendering (IBR) methods have recently been employed to synthesize high-quality novel views, although they require an excessive number of adjacent input images or detailed geometry just to render local views. While the development of global data, such as Google Street View, has accelerated interactive IBR techniques for urban scenes, such methods have hardly been aimed at high-quality street-level rendering. To provide users with free walk-through experiences in global urban streets, our system effectively covers large-scale scenes by using sparsely sampled panoramic street-view images and simplified scene models, which are easily obtainable from open databases. Our key concept is to extract semantic information from the given street-view images and to deploy it in proper intermediate steps of the suggested pipeline, which results in enhanced rendering accuracy and performance time. Furthermore, our method supports real-time semantic 3D inpainting to handle occluded and untextured areas, which appear often when the user's viewpoint dynamically changes. Experimental results validate the effectiveness of this method in comparison with the state-of-the-art approaches. We also present real-time demos in various urban streets.

We present a novel natural user interface framework, called Meta-Gesture, for selecting and manipulating rotatable virtual reality (VR) objects in egocentric viewpoint. Meta-Gesture uses the gestures of holding and manipulating the tools of daily use. Specifically, the holding gesture is used to summon a virtual object into the palm, and the manipulating gesture to trigger the function of the summoned virtual tool. Our contributions are broadly threefold: 1) Meta-Gesture is the first to perform bare hand-gesture-based orientation-aware selection and manipulation of very small (nail-sized) VR objects, which has become possible by combining a stable 3-D palm pose estimator (publicly available) with the proposed static-dynamic (SD) gesture estimator; 2) the proposed novel SD random forest, as an SD gesture estimator can classify a 3-D static gesture and its action status hierarchically, in a single classifier; and 3) our novel voxel coding scheme, called layered shape pattern, which is configured by calculating the fill rate of point clouds (raw source of data) in each voxel on the top of the palm pose estimation, allows for dispensing with the need for preceding hand skeletal tracking or joint classification while defining a gesture. Experimental results show that the proposed method can deliver promising performance, even under frequent occlusions, during orientation-aware selection and manipulation of objects in VR space by wearing head-mounted display with an attached egocentric-depth camera (see the supplementary video available at: http://ieeexplore.ieee.org).