EgoFace: Egocentric Face Performance Capture and Videorealistic Reenactment

Face performance capture and reenactment techniques use multiple cameras and sensors, positioned at a distance from the face or mounted on heavy wearable devices. This limits their applications in mobile and outdoor environments. We present EgoFace, a radically new lightweight setup for face performance capture and front-view videorealistic reenactment using a single egocentric RGB camera. Our lightweight setup allows operations in uncontrolled environments, and lends itself to telepresence applications such as video-conferencing from dynamic environments. The input image is projected into a low dimensional latent space of the facial expression parameters. Through careful adversarial training of the parameter-space synthetic rendering, a videorealistic animation is produced. Our problem is challenging as the human visual system is sensitive to the smallest face irregularities that could occur in the final results. This sensitivity is even stronger for video results. Our solution is trained in a pre-processing stage, through a supervised manner without manual annotations. EgoFace captures a wide variety of facial expressions, including mouth movements and asymmetrical expressions. It works under varying illuminations, background, movements, handles people from different ethnicities and can operate in real time

Egocentric Reconstruction of Human Bodies for Real-time Mobile Telepresence

A mobile 3D acquisition system has the potential to make telepresence significantly more convenient, available to users anywhere, anytime, without relying on any instrumented environments. Such a system can be implemented using egocentric reconstruction methods, which rely only on wearable sensors, such as head-worn cameras and body-worn inertial measurement units. Prior egocentric reconstruction methods suffer from incomplete body visibility as well as insufficient sensor data. This dissertation investigates an egocentric 3D capture system relying only on sensors embedded in commonly worn items such as eyeglasses, wristwatches, and shoes. It introduces three advances in egocentric reconstruction of human bodies. (1) A parametric-model-based reconstruction method that overcomes incomplete body surface visibility by estimating the user's body pose and facial expression, and using the results to re-target a high-fidelity pre-scanned model of the user. (2) A learning-based visual-inertial body motion reconstruction system that relies only on eyeglasses-mounted cameras and a few body-worn inertial sensors. This approach overcomes the challenges of self-occlusion and outside-of-camera motions, and allows for unobtrusive real-time 3D capture of the user. (3) A physically plausible reconstruction method based on rigid body dynamics, which reduces motion jitter and prevents interpenetrations between the reconstructed user's model and the objects in the environment such as the ground, walls, and furniture. This dissertation includes experimental results demonstrating the real-time, mobile reconstruction of human bodies in indoor and outdoor scenes, relying only on wearable sensors embedded in commonly-worn objects and overcoming the sparse observation challenges of egocentric reconstruction. The potential usefulness of this approach is demonstrated in a telepresence scenario featuring physical therapy training.Doctor of Philosoph

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

Eyewear Computing \u2013 Augmenting the Human with Head-Mounted Wearable Assistants

The seminar was composed of workshops and tutorials on head-mounted eye tracking, egocentric vision, optics, and head-mounted displays. The seminar welcomed 30 academic and industry researchers from Europe, the US, and Asia with a diverse background, including wearable and ubiquitous computing, computer vision, developmental psychology, optics, and human-computer interaction. In contrast to several previous Dagstuhl seminars, we used an ignite talk format to reduce the time of talks to one half-day and to leave the rest of the week for hands-on sessions, group work, general discussions, and socialising. The key results of this seminar are 1) the identification of key research challenges and summaries of breakout groups on multimodal eyewear computing, egocentric vision, security and privacy issues, skill augmentation and task guidance, eyewear computing for gaming, as well as prototyping of VR applications, 2) a list of datasets and research tools for eyewear computing, 3) three small-scale datasets recorded during the seminar, 4) an article in ACM Interactions entitled \u201cEyewear Computers for Human-Computer Interaction\u201d, as well as 5) two follow-up workshops on \u201cEgocentric Perception, Interaction, and Computing\u201d at the European Conference on Computer Vision (ECCV) as well as \u201cEyewear Computing\u201d at the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)

Acting rehearsal in collaborative multimodal mixed reality environments

This paper presents the use of our multimodal mixed reality telecommunication system to support remote acting rehearsal. The rehearsals involved two actors, located in London and Barcelona, and a director in another location in London. This triadic audiovisual telecommunication was performed in a spatial and multimodal collaborative mixed reality environment based on the 'destination-visitor' paradigm, which we define and put into use. We detail our heterogeneous system architecture, which spans the three distributed and technologically asymmetric sites, and features a range of capture, display, and transmission technologies. The actors' and director's experience of rehearsing a scene via the system are then discussed, exploring successes and failures of this heterogeneous form of telecollaboration. Overall, the common spatial frame of reference presented by the system to all parties was highly conducive to theatrical acting and directing, allowing blocking, gross gesture, and unambiguous instruction to be issued. The relative inexpressivity of the actors' embodiments was identified as the central limitation of the telecommunication, meaning that moments relying on performing and reacting to consequential facial expression and subtle gesture were less successful

Requirement analysis and sensor specifications – First version

In this first version of the deliverable, we make the following contributions: to design the WEKIT capturing platform and the associated experience capturing API, we use a methodology for system engineering that is relevant for different domains such as: aviation, space, and medical and different professions such as: technicians, astronauts, and medical staff. Furthermore, in the methodology, we explore the system engineering process and how it can be used in the project to support the different work packages and more importantly the different deliverables that will follow the current. Next, we provide a mapping of high level functions or tasks (associated with experience transfer from expert to trainee) to low level functions such as: gaze, voice, video, body posture, hand gestures, bio-signals, fatigue levels, and location of the user in the environment. In addition, we link the low level functions to their associated sensors. Moreover, we provide a brief overview of the state-of-the-art sensors in terms of their technical specifications, possible limitations, standards, and platforms. We outline a set of recommendations pertaining to the sensors that are most relevant for the WEKIT project taking into consideration the environmental, technical and human factors described in other deliverables. We recommend Microsoft Hololens (for Augmented reality glasses), MyndBand and Neurosky chipset (for EEG), Microsoft Kinect and Lumo Lift (for body posture tracking), and Leapmotion, Intel RealSense and Myo armband (for hand gesture tracking). For eye tracking, an existing eye-tracking system can be customised to complement the augmented reality glasses, and built-in microphone of the augmented reality glasses can capture the expert’s voice. We propose a modular approach for the design of the WEKIT experience capturing system, and recommend that the capturing system should have sufficient storage or transmission capabilities. Finally, we highlight common issues associated with the use of different sensors. We consider that the set of recommendations can be useful for the design and integration of the WEKIT capturing platform and the WEKIT experience capturing API to expedite the time required to select the combination of sensors which will be used in the first prototype.WEKI