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

Fully Automatic Multi-Object Articulated Motion Tracking

Fully automatic tracking of articulated motion in real-time with a monocular RGB camera is a challenging problem which is essential for many virtual reality (VR) and human-computer interaction applications. In this paper, we present an algorithm for multiple articulated objects tracking based on monocular RGB image sequence. Our algorithm can be directly employed in practical applications as it is fully automatic, real-time, and temporally stable. It consists of the following stages: dynamic objects counting, objects specific 3D skeletons generation, initial 3D poses estimation, and 3D skeleton fitting which fits each 3D skeleton to the corresponding 2D body-parts locations. In the skeleton fitting stage, the 3D pose of every object is estimated by maximizing an objective function that combines a skeleton fitting term with motion and pose priors. To illustrate the importance of our algorithm for practical applications, we present competitive results for real-time tracking of multiple humans. Our algorithm detects objects that enter or leave the scene, and dynamically generates or deletes their 3D skeletons. This makes our monocular RGB method optimal for real-time applications. We show that our algorithm is applicable for tracking multiple objects in outdoor scenes, community videos, and low-quality videos captured with mobile-phone cameras. Keywords: Multi-object motion tracking, Articulated motion capture, Deep learning, Anthropometric data, 3D pose estimation. DOI: 10.7176/CEIS/12-1-01 Publication date: March 31st 202

Towards High-Frequency Tracking and Fast Edge-Aware Optimization

This dissertation advances the state of the art for AR/VR tracking systems by increasing the tracking frequency by orders of magnitude and proposes an efficient algorithm for the problem of edge-aware optimization. AR/VR is a natural way of interacting with computers, where the physical and digital worlds coexist. We are on the cusp of a radical change in how humans perform and interact with computing. Humans are sensitive to small misalignments between the real and the virtual world, and tracking at kilo-Hertz frequencies becomes essential. Current vision-based systems fall short, as their tracking frequency is implicitly limited by the frame-rate of the camera. This thesis presents a prototype system which can track at orders of magnitude higher than the state-of-the-art methods using multiple commodity cameras. The proposed system exploits characteristics of the camera traditionally considered as flaws, namely rolling shutter and radial distortion. The experimental evaluation shows the effectiveness of the method for various degrees of motion. Furthermore, edge-aware optimization is an indispensable tool in the computer vision arsenal for accurate filtering of depth-data and image-based rendering, which is increasingly being used for content creation and geometry processing for AR/VR. As applications increasingly demand higher resolution and speed, there exists a need to develop methods that scale accordingly. This dissertation proposes such an edge-aware optimization framework which is efficient, accurate, and algorithmically scales well, all of which are much desirable traits not found jointly in the state of the art. The experiments show the effectiveness of the framework in a multitude of computer vision tasks such as computational photography and stereo.Comment: PhD thesi

Analysis of the hands in egocentric vision: A survey

Egocentric vision (a.k.a. first-person vision - FPV) applications have thrived over the past few years, thanks to the availability of affordable wearable cameras and large annotated datasets. The position of the wearable camera (usually mounted on the head) allows recording exactly what the camera wearers have in front of them, in particular hands and manipulated objects. This intrinsic advantage enables the study of the hands from multiple perspectives: localizing hands and their parts within the images; understanding what actions and activities the hands are involved in; and developing human-computer interfaces that rely on hand gestures. In this survey, we review the literature that focuses on the hands using egocentric vision, categorizing the existing approaches into: localization (where are the hands or parts of them?); interpretation (what are the hands doing?); and application (e.g., systems that used egocentric hand cues for solving a specific problem). Moreover, a list of the most prominent datasets with hand-based annotations is provided

Interaction Replica: Tracking human-object interaction and scene changes from human motion

Humans naturally change their environment through interactions, e.g., by opening doors or moving furniture. To reproduce such interactions in virtual spaces (e.g., metaverse), we need to capture and model them, including changes in the scene geometry, ideally from egocentric input alone (head camera and body-worn inertial sensors). While the head camera can be used to localize the person in the scene, estimating dynamic object pose is much more challenging. As the object is often not visible from the head camera (e.g., a human not looking at a chair while sitting down), we can not rely on visual object pose estimation. Instead, our key observation is that human motion tells us a lot about scene changes. Motivated by this, we present iReplica, the first human-object interaction reasoning method which can track objects and scene changes based solely on human motion. iReplica is an essential first step towards advanced AR/VR applications in immersive virtual universes and can provide human-centric training data to teach machines to interact with their surroundings. Our code, data and model will be available on our project page at http://virtualhumans.mpi-inf.mpg.de/ireplica

Application of augmented reality and robotic technology in broadcasting: A survey

As an innovation technique, Augmented Reality (AR) has been gradually deployed in the broadcast, videography and cinematography industries. Virtual graphics generated by AR are dynamic and overlap on the surface of the environment so that the original appearance can be greatly enhanced in comparison with traditional broadcasting. In addition, AR enables broadcasters to interact with augmented virtual 3D models on a broadcasting scene in order to enhance the performance of broadcasting. Recently, advanced robotic technologies have been deployed in a camera shooting system to create a robotic cameraman so that the performance of AR broadcasting could be further improved, which is highlighted in the paper

Learning to see and hear in 3D: Virtual reality as a platform for multisensory perceptual learning

Virtual reality (VR) is an emerging technology which allows for the presentation of immersive and realistic yet tightly controlled audiovisual scenes. In comparison to conventional displays, the VR system can include depth, 3D audio, fully integrated eye, head, and hand tracking, all over a much larger field of view than a desktop monitor provides. These properties demonstrate great potential for use in vision science experiments, especially those that can benefit from more naturalistic stimuli, particularly in the case of visual rehabilitation. Prior work using conventional displays has demonstrated that that visual loss due to stroke can be partially rehabilitated through laboratory-based tasks designed to promote long-lasting changes to visual sensitivity. In this work, I will explore how VR can provide a platform for new, more complex training paradigms which leverage multisensory stimuli. In this dissertation, I will (I) provide context to motivate the use of multisensory perceptual training in the context of visual rehabilitation, (II) demonstrate best practices for the appropriate use of VR in a controlled psychophysics setting, (III) describe a prototype integrated hardware system for improved eye tracking in VR, and (IV, V) discuss results from two audiovisual perceptual training studies, one using multisensory stimuli and the other with cross-modal audiovisual stimuli. This dissertation provides the foundation for future work in rehabilitating visual deficits, by both improving the hardware and software systems used to present the training paradigm as well as validating new techniques which use multisensory training not previously accessible with conventional desktop displays