Die immersive Medieninstallation personal fractalis II (pf.2) lädt die Besucher:innen dazu ein, sich in eine immersive generierte Welt aus Fraktalen ziehen zu lassen – die Benutzer:in exploriert frei in dieser utopischen Umgebung oder begibt sich auf den Weg einer durch Autor:in und künstlicher Intelligenz kuratierten Entdeckungsreise.
Face-to-face conversation in Virtual Reality (VR) is a challenge when participants wear head-mounted displays (HMD). A significant portion of a participant’s face is hidden and facial expressions are difficult to perceive. Past research has shown that high-fidelity face reconstruction with personal avatars in VR is possible under laboratory conditions with high-cost hardware. In this paper, we propose one of the first low-cost systems for this task which uses only open source, free software and affordable hardware.
Philipp Ladwig held a talk at the 17th GI VR/AR workshop about our research in the area of telepresence in virtual reality applications. The associated paper written by Philipp Ladwig and Alexander Pech under the guidance of Prof. Dr. Christian Geiger received the "Best Paper" award.
The objective of our research is to enhance the creation of interactive environments such as in VR applications. An interactive environment can be produced from a point cloud that is acquired by a 3D scanning process of a certain scenery. The segmentation is needed to extract objects in that point cloud to, e.g., apply certain physical properties to them in a further step. It takes a lot of effort to do this manually as single objects have to be extracted and post-processed. Thus, our research aim is the real-world, cross-domain, automatic, semantic segmentation without the estimation of specific object classes.
Three-dimensional capture of faces with personal expressions for facial reconstruction under a head-mounted display.
We propose a method to segment a real world point cloud as perceptual grouping task (PGT) by a deep reinforcement learning (DRL) agent. A point cloud is divided into groups of points, named superpoints, for the PGT. These superpoints should be grouped to objects by a deep neural network policy that is optimised by a DRL algorithm.
The segmentation of point clouds is conducted with the help of deep reinforcement learning (DRL) in this contribution. We want to create interactive virtual reality (VR) environments from point cloud scans as fast as possible. These VR environments are used for secure and immersive trainings of serious real life applications such as the extinguishing of a fire. It is necessary to segment the point cloud scans to create interactions in the VR
Skype, Facetime and similar apps have become an integral part of our everyday life. However, the digital meeting does not compare to the real, physical one. Both spatial perception and non-verbal communication are limited by the current digital channels.
Our goal is to create realistic 3D images of objects or rooms. For this purpose, we use the laser scanner Faro Focus S70, which can capture anything visible in a sphere with a radius of up to 70 meters. Whenever we scan a room, we also move along the walls and take overlapping photographs. Overall, depending on the size of the room or object, we collect between 600 and 1500 pictures to ensure that we obtain enough material in a single scanning process.
In diesem Paper wird ein neuartiger Ansatz eines Fingertracking-Systems auf Basis neuronaler Netze vorgestellt. Dazu werden mithilfe eines externen, optischen Trackingsystems kleine Marker (Ø 4 mm) an den Fingerspitzen erkannt, womit zwei neuronale Netze die Positionen aller Fingergelenke bestimmen.