Bare-handed 3D drawing in augmented reality

Head-mounted augmented reality (AR) enables embodied in situ drawing in three dimensions (3D).We explore 3D drawing interactions based on uninstrumented, unencumbered (bare) hands that preserve the user’s ability to freely navigate and interact with the physical environment. We derive three alternative interaction techniques supporting bare-handed drawing in AR from the literature and by analysing several envisaged use cases. The three interaction techniques are evaluated in a controlled user study examining three distinct drawing tasks: planar drawing, path description, and 3D object reconstruction. The results indicate that continuous freehand drawing supports faster line creation than the control point-based alternatives, although with reduced accuracy. User preferences for the different techniques are mixed and vary considerably between the different tasks, highlighting the value of diverse and flexible interactions. The combined effectiveness of these three drawing techniques is illustrated in an example application of 3D AR drawing

HandPainter – 3D sketching in VR with hand-based physical proxy

3D sketching in virtual reality (VR) enables users to create 3D virtual objects intuitively and immersively. However, previous studies showed that mid-air drawing may lead to inaccurate sketches. To address this issue, we propose to use one hand as a canvas proxy and the index finger of the other hand as a 3D pen. To this end, we first perform a formative study to compare two-handed interaction with tablet-pen interaction for VR sketching. Based on the findings of this study, we design HandPainter, a VR sketching system which focuses on the direct use of two hands for 3D sketching without requesting any tablet, pen, or VR controller. Our implementation is based on a pair of VR gloves, which provide hand tracking and gesture capture. We devise a set of intuitive gestures to control various functionalities required during 3D sketching, such as canvas panning and drawing positioning. We show the effectiveness of HandPainter by presenting a number of sketching results and discussing the outcomes of a user study-based comparison with mid-air drawing and tablet-based sketching tools

WristSketcher: Creating Dynamic Sketches in AR with a Sensing Wristband

Restricted by the limited interaction area of native AR glasses (e.g., touch bars), it is challenging to create sketches in AR glasses. Recent works have attempted to use mobile devices (e.g., tablets) or mid-air bare-hand gestures to expand the interactive spaces and can work as the 2D/3D sketching input interfaces for AR glasses. Between them, mobile devices allow for accurate sketching but are often heavy to carry, while sketching with bare hands is zero-burden but can be inaccurate due to arm instability. In addition, mid-air bare-hand sketching can easily lead to social misunderstandings and its prolonged use can cause arm fatigue. As a new attempt, in this work, we present WristSketcher, a new AR system based on a flexible sensing wristband for creating 2D dynamic sketches, featuring an almost zero-burden authoring model for accurate and comfortable sketch creation in real-world scenarios. Specifically, we have streamlined the interaction space from the mid-air to the surface of a lightweight sensing wristband, and implemented AR sketching and associated interaction commands by developing a gesture recognition method based on the sensing pressure points on the wristband. The set of interactive gestures used by our WristSketcher is determined by a heuristic study on user preferences. Moreover, we endow our WristSketcher with the ability of animation creation, allowing it to create dynamic and expressive sketches. Experimental results demonstrate that our WristSketcher i) faithfully recognizes users' gesture interactions with a high accuracy of 96.0%; ii) achieves higher sketching accuracy than Freehand sketching; iii) achieves high user satisfaction in ease of use, usability and functionality; and iv) shows innovation potentials in art creation, memory aids, and entertainment applications

Barehand Mode Switching in Touch and Mid-Air Interfaces

Raskin defines a mode as a distinct setting within an interface where the same user input will produce results different to those it would produce in other settings. Most interfaces have multiple modes in which input is mapped to different actions, and, mode-switching is simply the transition from one mode to another. In touch interfaces, the current mode can change how a single touch is interpreted: for example, it could draw a line, pan the canvas, select a shape, or enter a command. In Virtual Reality (VR), a hand gesture-based 3D modelling application may have different modes for object creation, selection, and transformation. Depending on the mode, the movement of the hand is interpreted differently. However, one of the crucial factors determining the effectiveness of an interface is user productivity. Mode-switching time of different input techniques, either in a touch interface or in a mid-air interface, affects user productivity. Moreover, when touch and mid-air interfaces like VR are combined, making informed decisions pertaining to the mode assignment gets even more complicated. This thesis provides an empirical investigation to characterize the mode switching phenomenon in barehand touch-based and mid-air interfaces. It explores the potential of using these input spaces together for a productivity application in VR. And, it concludes with a step towards defining and evaluating the multi-faceted mode concept, its characteristics and its utility, when designing user interfaces more generally

Interaction Methods for Smart Glasses : A Survey

Since the launch of Google Glass in 2014, smart glasses have mainly been designed to support micro-interactions. The ultimate goal for them to become an augmented reality interface has not yet been attained due to an encumbrance of controls. Augmented reality involves superimposing interactive computer graphics images onto physical objects in the real world. This survey reviews current research issues in the area of human-computer interaction for smart glasses. The survey first studies the smart glasses available in the market and afterwards investigates the interaction methods proposed in the wide body of literature. The interaction methods can be classified into hand-held, touch, and touchless input. This paper mainly focuses on the touch and touchless input. Touch input can be further divided into on-device and on-body, while touchless input can be classified into hands-free and freehand. Next, we summarize the existing research efforts and trends, in which touch and touchless input are evaluated by a total of eight interaction goals. Finally, we discuss several key design challenges and the possibility of multi-modal input for smart glasses.Peer reviewe

To Draw or Not to Draw: Recognizing Stroke-Hover Intent in Gesture-Free Bare-Hand Mid-Air Drawing Tasks

Over the past several decades, technological advancements have introduced new modes of communication with the computers, introducing a shift from traditional mouse and keyboard interfaces. While touch based interactions are abundantly being used today, latest developments in computer vision, body tracking stereo cameras, and augmented and virtual reality have now enabled communicating with the computers using spatial input in the physical 3D space. These techniques are now being integrated into several design critical tasks like sketching, modeling, etc. through sophisticated methodologies and use of specialized instrumented devices. One of the prime challenges in design research is to make this spatial interaction with the computer as intuitive as possible for the users. Drawing curves in mid-air with fingers, is a fundamental task with applications to 3D sketching, geometric modeling, handwriting recognition, and authentication. Sketching in general, is a crucial mode for effective idea communication between designers. Mid-air curve input is typically accomplished through instrumented controllers, specific hand postures, or pre-defined hand gestures, in presence of depth and motion sensing cameras. The user may use any of these modalities to express the intention to start or stop sketching. However, apart from suffering with issues like lack of robustness, the use of such gestures, specific postures, or the necessity of instrumented controllers for design specific tasks further result in an additional cognitive load on the user. To address the problems associated with different mid-air curve input modalities, the presented research discusses the design, development, and evaluation of data driven models for intent recognition in non-instrumented, gesture-free, bare-hand mid-air drawing tasks. The research is motivated by a behavioral study that demonstrates the need for such an approach due to the lack of robustness and intuitiveness while using hand postures and instrumented devices. The main objective is to study how users move during mid-air sketching, develop qualitative insights regarding such movements, and consequently implement a computational approach to determine when the user intends to draw in mid-air without the use of an explicit mechanism (such as an instrumented controller or a specified hand-posture). By recording the user’s hand trajectory, the idea is to simply classify this point as either hover or stroke. The resulting model allows for the classification of points on the user’s spatial trajectory. Drawing inspiration from the way users sketch in mid-air, this research first specifies the necessity for an alternate approach for processing bare hand mid-air curves in a continuous fashion. Further, this research presents a novel drawing intent recognition work flow for every recorded drawing point, using three different approaches. We begin with recording mid-air drawing data and developing a classification model based on the extracted geometric properties of the recorded data. The main goal behind developing this model is to identify drawing intent from critical geometric and temporal features. In the second approach, we explore the variations in prediction quality of the model by improving the dimensionality of data used as mid-air curve input. Finally, in the third approach, we seek to understand the drawing intention from mid-air curves using sophisticated dimensionality reduction neural networks such as autoencoders. Finally, the broad level implications of this research are discussed, with potential development areas in the design and research of mid-air interactions

Real-Time Markerless Tracking the Human Hands for 3D Interaction

This thesis presents methods for enabling suitable human computer interaction using only movements of the bare human hands in free space. This kind of interaction is natural and intuitive, particularly because actions familiar to our everyday life can be reflected. Furthermore, the input is contact-free which is of great advantage e.g. in medical applications due to hygiene factors. For enabling the translation of hand movements to control signals an automatic method for tracking the pose and/or posture of the hand is needed. In this context the simultaneous recognition of both hands is desirable to allow for more natural input. The first contribution of this thesis is a novel video-based method for real-time detection of the positions and orientations of both bare human hands in four different predefined postures, respectively. Based on such a system novel interaction interfaces can be developed. However, the design of such interfaces is a non-trivial task. Additionally, the development of novel interaction techniques is often mandatory in order to enable the design of efficient and easily operable interfaces. To this end, several novel interaction techniques are presented and investigated in this thesis, which solve existing problems and substantially improve the applicability of such a new device. These techniques are not restricted to this input instrument and can also be employed to improve the handling of other interaction devices. Finally, several new interaction interfaces are described and analyzed to demonstrate possible applications in specific interaction scenarios.Markerlose Verfolgung der menschlichen Hände in Echtzeit für 3D Interaktion In der vorliegenden Arbeit werden Verfahren dargestellt, die sinnvolle Mensch- Maschine-Interaktionen nur durch Bewegungen der bloßen Hände in freiem Raum ermöglichen. Solche "natürlichen" Interaktionen haben den besonderen Vorteil, dass alltägliche und vertraute Handlungen in die virtuelle Umgebung übertragen werden können. Außerdem werden auf diese Art berührungslose Eingaben ermöglicht, nützlich z.B. wegen hygienischer Aspekte im medizinischen Bereich. Um Handbewegungen in Steuersignale umsetzen zu können, ist zunächst ein automatisches Verfahren zur Erkennung der Lage und/oder der Art der mit der Hand gebildeten Geste notwendig. Dabei ist die gleichzeitige Erfassung beider Hände wünschenswert, um die Eingaben möglichst natürlich gestalten zu können. Der erste Beitrag dieser Arbeit besteht aus einer neuen videobasierten Methode zur unmittelbaren Erkennung der Positionen und Orientierungen beider Hände in jeweils vier verschiedenen, vordefinierten Gesten. Basierend auf einem solchen Verfahren können neuartige Interaktionsschnittstellen entwickelt werden. Allerdings ist die Ausgestaltung solcher Schnittstellen keinesfalls trivial. Im Gegenteil ist bei einer neuen Art der Interaktion meist sogar die Entwicklung neuer Interaktionstechniken erforderlich, damit überhaupt effiziente und gut bedienbare Schnittstellen konzipiert werden können. Aus diesem Grund wurden in dieser Arbeit einige neue Interaktionstechniken entwickelt und untersucht, die vorhandene Probleme beheben und die Anwendbarkeit eines solchen Eingabeinstruments für bestimmte Arten der Interaktion verbessern oder überhaupt erst ermöglichen. Diese Techniken sind nicht auf dieses Eingabeinstrument beschränkt und können durchaus auch die Handhabung anderer Eingabegeräte verbessern. Des Weiteren werden mehrere neue Interaktionsschnittstellen präsentiert, die den möglichen Einsatz bloßhändiger Interaktion in verschiedenen, typischen Anwendungsgebieten veranschaulichen

Get a Grip: Evaluating Grip Gestures for VR Input Using a Lightweight Pen

The use of Virtual Reality (VR) in applications such as data analysis, artistic creation, and clinical settings requires high precision input. However, the current design of handheld controllers, where wrist rotation is the primary input approach, does not exploit the human fingers' capability for dexterous movements for high precision pointing and selection. To address this issue, we investigated the characteristics and potential of using a pen as a VR input device. We conducted two studies. The first examined which pen grip allowed the largest range of motion---we found a tripod grip at the rear end of the shaft met this criterion. The second study investigated target selection via 'poking' and ray-casting, where we found the pen grip outperformed the traditional wrist-based input in both cases. Finally, we demonstrate potential applications enabled by VR pen input and grip postures