Analysis of Locally Coupled 3D Manipulation Mappings Based on Mobile Device Motion

We examine a class of techniques for 3D object manipulation on mobile devices, in which the device's physical motion is applied to 3D objects displayed on the device itself. This "local coupling" between input and display creates specific challenges compared to manipulation techniques designed for monitor-based or immersive virtual environments. Our work focuses specifically on the mapping between device motion and object motion. We review existing manipulation techniques and introduce a formal description of the main mappings under a common notation. Based on this notation, we analyze these mappings and their properties in order to answer crucial usability questions. We first investigate how the 3D objects should move on the screen, since the screen also moves with the mobile device during manipulation. We then investigate the effects of a limited range of manipulation and present a number of solutions to overcome this constraint. This work provides a theoretical framework to better understand the properties of locally-coupled 3D manipulation mappings based on mobile device motion

Vision based 3D Gesture Tracking using Augmented Reality and Virtual Reality for Improved Learning Applications

3D gesture recognition and tracking based augmented reality and virtual reality have become a big interest of research because of advanced technology in smartphones. By interacting with 3D objects in augmented reality and virtual reality, users get better understanding of the subject matter where there have been requirements of customized hardware support and overall experimental performance needs to be satisfactory. This research investigates currently various vision based 3D gestural architectures for augmented reality and virtual reality. The core goal of this research is to present analysis on methods, frameworks followed by experimental performance on recognition and tracking of hand gestures and interaction with virtual objects in smartphones. This research categorized experimental evaluation for existing methods in three categories, i.e. hardware requirement, documentation before actual experiment and datasets. These categories are expected to ensure robust validation for practical usage of 3D gesture tracking based on augmented reality and virtual reality. Hardware set up includes types of gloves, fingerprint and types of sensors. Documentation includes classroom setup manuals, questionaries, recordings for improvement and stress test application. Last part of experimental section includes usage of various datasets by existing research. The overall comprehensive illustration of various methods, frameworks and experimental aspects can significantly contribute to 3D gesture recognition and tracking based augmented reality and virtual reality.Peer reviewe

The Role of Effector Physicality and Risk Perception in Virtual Environments

Research has consistently demonstrated that people treat digital technology-based environments such as VR as if they were real. This is consistent with neural reuse and predictive processing theories. Neural circuits that have developed to perform real world actions are reused when performing tasks in computer mediated environments. The current research investigates some of the factors that could support users in leveraging their existing real world representations. A reasonable hypothesis is that users are more likely to emulate existing real world processing if technological artifacts are congruent with their experiential basis. This work investigates the perceived cues of task risks, movement realism and effector realism in performing actions. Effector design is manipulated (gesturing, wand, vs. knife), and participants cut a vegetable in a simulated environment. Participants evoked real world sensory motor contingency when technological artifacts are congruent with their experiential basis

{Mo2Cap2}: Real-time Mobile {3D} Motion Capture with a Cap-mounted Fisheye Camera

We propose the first real-time approach for the egocentric estimation of 3D human body pose in a wide range of unconstrained everyday activities. This setting has a unique set of challenges, such as mobility of the hardware setup, and robustness to long capture sessions with fast recovery from tracking failures. We tackle these challenges based on a novel lightweight setup that converts a standard baseball cap to a device for high-quality pose estimation based on a single cap-mounted fisheye camera. From the captured egocentric live stream, our CNN based 3D pose estimation approach runs at 60Hz on a consumer-level GPU. In addition to the novel hardware setup, our other main contributions are: 1) a large ground truth training corpus of top-down fisheye images and 2) a novel disentangled 3D pose estimation approach that takes the unique properties of the egocentric viewpoint into account. As shown by our evaluation, we achieve lower 3D joint error as well as better 2D overlay than the existing baselines

GRAB: A Dataset of Whole-Body Human Grasping of Objects

Training computers to understand, model, and synthesize human grasping requires a rich dataset containing complex 3D object shapes, detailed contact information, hand pose and shape, and the 3D body motion over time. While "grasping" is commonly thought of as a single hand stably lifting an object, we capture the motion of the entire body and adopt the generalized notion of "whole-body grasps". Thus, we collect a new dataset, called GRAB (GRasping Actions with Bodies), of whole-body grasps, containing full 3D shape and pose sequences of 10 subjects interacting with 51 everyday objects of varying shape and size. Given MoCap markers, we fit the full 3D body shape and pose, including the articulated face and hands, as well as the 3D object pose. This gives detailed 3D meshes over time, from which we compute contact between the body and object. This is a unique dataset, that goes well beyond existing ones for modeling and understanding how humans grasp and manipulate objects, how their full body is involved, and how interaction varies with the task. We illustrate the practical value of GRAB with an example application; we train GrabNet, a conditional generative network, to predict 3D hand grasps for unseen 3D object shapes. The dataset and code are available for research purposes at https://grab.is.tue.mpg.de.Comment: ECCV 202

A Survey on Augmented Reality Challenges and Tracking

This survey paper presents a classification of different challenges and tracking techniques in the field of augmented reality. The challenges in augmented reality are categorized into performance challenges, alignment challenges, interaction challenges, mobility/portability challenges and visualization challenges. Augmented reality tracking techniques are mainly divided into sensor-based tracking, visionbased tracking and hybrid tracking. The sensor-based tracking is further divided into optical tracking, magnetic tracking, acoustic tracking, inertial tracking or any combination of these to form hybrid sensors tracking. Similarly, the vision-based tracking is divided into marker-based tracking and markerless tracking. Each tracking technique has its advantages and limitations. Hybrid tracking provides a robust and accurate tracking but it involves financial and tehnical difficulties