A comparison of guiding techniques for out-of-view objects in full-coverage displays

Full-coverage displays can place visual content anywhere on the interior surfaces of a room (e.g., a weather display near the coat stand). In these settings, digital artefacts can be located behind the user and out of their field of view - meaning that it can be difficult to notify the user when these artefacts need attention. Although much research has been carried out on notification, little is known about how best to direct people to the necessary location in room environments. We designed five diverse attention-guiding techniques for full-coverage display rooms, and evaluated them in a study where participants completed search tasks guided by the different techniques. Our study provides new results about notification in full-coverage displays: we showed benefits of persistent visualisations that could be followed all the way to the target and that indicate distance-to-target. Our findings provide useful information for improving the usability of interactive full-coverage environments.PostprintPostprin

REAL-TIME CAPTURE AND RENDERING OF PHYSICAL SCENE WITH AN EFFICIENTLY CALIBRATED RGB-D CAMERA NETWORK

From object tracking to 3D reconstruction, RGB-Depth (RGB-D) camera networks play an increasingly important role in many vision and graphics applications. With the recent explosive growth of Augmented Reality (AR) and Virtual Reality (VR) platforms, utilizing camera RGB-D camera networks to capture and render dynamic physical space can enhance immersive experiences for users. To maximize coverage and minimize costs, practical applications often use a small number of RGB-D cameras and sparsely place them around the environment for data capturing. While sparse color camera networks have been studied for decades, the problems of extrinsic calibration of and rendering with sparse RGB-D camera networks are less well understood. Extrinsic calibration is difficult because of inappropriate RGB-D camera models and lack of shared scene features. Due to the significant camera noise and sparse coverage of the scene, the quality of rendering 3D point clouds is much lower compared with synthetic models. Adding virtual objects whose rendering depend on the physical environment such as those with reflective surfaces further complicate the rendering pipeline. In this dissertation, I propose novel solutions to tackle these challenges faced by RGB-D camera systems. First, I propose a novel extrinsic calibration algorithm that can accurately and rapidly calibrate the geometric relationships across an arbitrary number of RGB-D cameras on a network. Second, I propose a novel rendering pipeline that can capture and render, in real-time, dynamic scenes in the presence of arbitrary-shaped reflective virtual objects. Third, I have demonstrated a teleportation application that uses the proposed system to merge two geographically separated 3D captured scenes into the same reconstructed environment. To provide a fast and robust calibration for a sparse RGB-D camera network, first, the correspondences between different camera views are established by using a spherical calibration object. We show that this approach outperforms other techniques based on planar calibration objects. Second, instead of modeling camera extrinsic using rigid transformation that is optimal only for pinhole cameras, different view transformation functions including rigid transformation, polynomial transformation, and manifold regression are systematically tested to determine the most robust mapping that generalizes well to unseen data. Third, the celebrated bundle adjustment procedure is reformulated to minimize the global 3D projection error so as to fine-tune the initial estimates. To achieve a realistic mirror rendering, a robust eye detector is used to identify the viewer\u27s 3D location and render the reflective scene accordingly. The limited field of view obtained from a single camera is overcome by our calibrated RGB-D camera network system that is scalable to capture an arbitrarily large environment. The rendering is accomplished by raytracing light rays from the viewpoint to the scene reflected by the virtual curved surface. To the best of our knowledge, the proposed system is the first to render reflective dynamic scenes from real 3D data in large environments. Our scalable client-server architecture is computationally efficient - the calibration of a camera network system, including data capture, can be done in minutes using only commodity PCs

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Actor & Avatar: A Scientific and Artistic Catalog

What kind of relationship do we have with artificial beings (avatars, puppets, robots, etc.)? What does it mean to mirror ourselves in them, to perform them or to play trial identity games with them? Actor & Avatar addresses these questions from artistic and scholarly angles. Contributions on the making of "technical others" and philosophical reflections on artificial alterity are flanked by neuroscientific studies on different ways of perceiving living persons and artificial counterparts. The contributors have achieved a successful artistic-scientific collaboration with extensive visual material

Embodiment Sensitivity to Movement Distortion and Perspective Taking in Virtual Reality

Despite recent technological improvements of immersive technologies, Virtual Reality suffers from severe intrinsic limitations, in particular the immateriality of the visible 3D environment. Typically, any simulation and manipulation in a cluttered environment would ideally require providing feedback of collisions to every body parts (arms, legs, trunk, etc.) and not only to the hands as has been originally explored with haptic feedback. This thesis addresses these limitations by relying on a cross modal perception and cognitive approach instead of haptic or force feedback. We base our design on scientific knowledge of bodily self-consciousness and embodiment. It is known that the instantaneous experience of embodiment emerges from the coherent multisensory integration of bodily signals taking place in the brain, and that altering this mechanism can temporarily change how one perceives properties of their own body. This mechanism is at stake during a VR simulation, and this thesis explores the new venues of interaction design based on these fundamental scientific findings about the embodied self. In particular, we explore the use of third person perspective (3PP) instead of permanently offering the traditional first person perspective (1PP), and we manipulate the user-avatar motor mapping to achieve a broader range of interactions while maintaining embodiment. We are guided by two principles, to explore the extent to which we can enhance VR interaction through the manipulation of bodily aspects, and to identify the extent to which a given manipulation affects the embodiment of a virtual body. Our results provide new evidence supporting strong embodiment of a virtual body even when viewed from 3PP, and in particular that voluntarily alternating point of view between 1PP and 3PP is not detrimental to the experience of ownership over the virtual body. Moreover, detailed analysis of movement quality show highly similar reaching behavior in both perspective conditions, and only obvious advantages or disadvantages of each perspective depending on the situation (e.g. occlusion of target by the body in 3PP, limited field of view in 1PP). We also show that subjects are insensitive to visuo-proprioceptive movement distortions when the nature of the distortion was not made explicit, and that subjects are biased toward self-attributing distorted movements that make the task easier

Data visualizing popular science fiction movies with use of circular hierarchical edge bundling

In this article, a specific type of data visualization method called Circular Hierarchical Edge Bundling has been utilized to investigate a subjective discussion on determining the most commonly observed themes in the popular Sci-Fi Movies. To reflect people’s opinions on the subject, a website (www.dystopia-utopia.com) has been designed to invite larger communities to participate in with filling an online form to deliver their judgments. Data Visualization methods and the research results are elaborated in further details