1,197 research outputs found
VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
We present the first real-time method to capture the full global 3D skeletal
pose of a human in a stable, temporally consistent manner using a single RGB
camera. Our method combines a new convolutional neural network (CNN) based pose
regressor with kinematic skeleton fitting. Our novel fully-convolutional pose
formulation regresses 2D and 3D joint positions jointly in real time and does
not require tightly cropped input frames. A real-time kinematic skeleton
fitting method uses the CNN output to yield temporally stable 3D global pose
reconstructions on the basis of a coherent kinematic skeleton. This makes our
approach the first monocular RGB method usable in real-time applications such
as 3D character control---thus far, the only monocular methods for such
applications employed specialized RGB-D cameras. Our method's accuracy is
quantitatively on par with the best offline 3D monocular RGB pose estimation
methods. Our results are qualitatively comparable to, and sometimes better
than, results from monocular RGB-D approaches, such as the Kinect. However, we
show that our approach is more broadly applicable than RGB-D solutions, i.e. it
works for outdoor scenes, community videos, and low quality commodity RGB
cameras.Comment: Accepted to SIGGRAPH 201
Novel Interaction Techniques for Mobile Augmented Reality applications. A Systematic Literature Review
This study reviews the research on interaction techniques and methods that could be applied in mobile augmented reality scenarios. The review is focused on themost recent advances and considers especially the use of head-mounted displays. Inthe review process, we have followed a systematic approach, which makes the reviewtransparent, repeatable, and less prone to human errors than if it was conducted in amore traditional manner. The main research subjects covered in the review are headorientation and gaze-tracking, gestures and body part-tracking, and multimodality– as far as the subjects are related to human-computer interaction. Besides these,also a number of other areas of interest will be discussed.Siirretty Doriast
An end-to-end review of gaze estimation and its interactive applications on handheld mobile devices
In recent years we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality. This trend is driven by the enhanced computational power of these devices, higher resolution and capacity of their cameras, and improved gaze estimation accuracy obtained from advanced machine learning techniques, especially in deep learning. As the literature is fast progressing, there is a pressing need to review the state of the art, delineate the boundary, and identify the key research challenges and opportunities in gaze estimation and interaction. This paper aims to serve this purpose by presenting an end-to-end holistic view in this area, from gaze capturing sensors, to gaze estimation workflows, to deep learning techniques, and to gaze interactive applications.PostprintPeer reviewe
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
Ambient Intelligence for Next-Generation AR
Next-generation augmented reality (AR) promises a high degree of
context-awareness - a detailed knowledge of the environmental, user, social and
system conditions in which an AR experience takes place. This will facilitate
both the closer integration of the real and virtual worlds, and the provision
of context-specific content or adaptations. However, environmental awareness in
particular is challenging to achieve using AR devices alone; not only are these
mobile devices' view of an environment spatially and temporally limited, but
the data obtained by onboard sensors is frequently inaccurate and incomplete.
This, combined with the fact that many aspects of core AR functionality and
user experiences are impacted by properties of the real environment, motivates
the use of ambient IoT devices, wireless sensors and actuators placed in the
surrounding environment, for the measurement and optimization of environment
properties. In this book chapter we categorize and examine the wide variety of
ways in which these IoT sensors and actuators can support or enhance AR
experiences, including quantitative insights and proof-of-concept systems that
will inform the development of future solutions. We outline the challenges and
opportunities associated with several important research directions which must
be addressed to realize the full potential of next-generation AR.Comment: This is a preprint of a book chapter which will appear in the
Springer Handbook of the Metavers
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