France

Extended reality (XR) is currently booming, and it is expected to dominate over the next few years. Brain-computer interfaces (BCIs) have been intensely researched to provide a method for controlling computers, robots, and other machinery using mental activity only. Nowadays, BCIs are becoming more and more appealing due to the recent advances in software and hardware technologies. However, the combination of BCIs and XR technologies is pretty much unexplored. This presentation will show the use of BCIs and in particular Electroencephalography (EEG) technologies for XR environments. In the first part, it will illustrate the strengths and weaknesses of the technology and how it can be integrated with XR. In the second part, different case studies will be presented demonstrating (a) how to perform novel experiments with the aim of understanding better human perception and (b) how to interact with brainwaves to control XR applications.

VISIGRAPP 2022

Brain Computer Interfaces for Extended Reality

The purpose of VISIGRAPP is to bring together researchers and practitioners interested in both theoretical advances and applications of computer vision, computer graphics and information visualization.

keynote

In this talk, I will show several recent results of my group on learning neural implicit 3D representations, departing from the traditional paradigm of representing 3D shapes explicitly using voxels, point clouds or meshes. Implicit representations have a small memory footprint and allow for modeling any 3D topology at arbitrary resolution in continuous function space. I will show the ability and limitations of these approaches in the context of reconstructing 3D geometry, texture and motion. I will further demonstrate a technique for learning implicit 3D models using only 2D supervision through implicit differentiation of the level set constraint. I will close with some applications from various domains including large-scale reconstruction, real-time novel view synthesis, generative modeling, human body estimation and self-driving.

Neural Implicit Representations for 3D Vision and Beyond

Human augmentation is an interdisciplinary field that addresses methods, technologies and their applications for enhancing sensing, action and/or cognitive abilities of a human. In this talk, I will discuss the past, present and future of human augmentation, focusing on non-invasive augmentation methods made possible through embodied multimodal interaction and augmented reality technologies. The talk will cover both potential benefits of human augmentation, as well as issues requiring further consideration, including societal and ethical concerns.

Wearable Human Augmentation

Spatial data visualized in geographic information displays (GIDs) are always subject to a multitude of inherent uncertainties. It is still an open research question, however, whether and how decision-makers need to be informed about potential data uncertainties, as misleading, or at worst, life-threatening outcomes might result from map-based decisions. As GIDs are becoming ubiquitous in the information society, as a way of communicating complex phenomena and processes to scientific experts and the general public alike, it raises the need and responsibility for scientists to visualize uncertainty. I will report on past and ongoing empirical geovisualization research with colleagues that investigates how data uncertainty visualized on maps might influence the process and outcomes of spatial decision-making, especially when made under time pressure, and in risky situations. Based on our collected empirical evidence to date, we argue that spatial data uncertainties should be communicated to space-time decision-makers, especially when decisions need to be made with limited time resources and when decision outcomes can have dramatic consequences.

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