Paul Zikas

Manos Kamarianakis, Antonis Protopsaltis, Dimitris Angelis et al.

This work presents the introduction of UniSG^GA, a novel integrated scenegraph structure, that to incorporates behavior and geometry data on a 3D scene. It is specifically designed to seamlessly integrate Graph Neural Networks (GNNs) and address the challenges associated with transforming a 3D scenegraph (3D-SG) during generative tasks. To effectively capture and preserve the topological relationships between objects in a simplified way, within the graph representation, we propose UniSG^GA, that seamlessly integrates Geometric Algebra (GA) forms. This novel approach enhances the overall performance and capability of GNNs in handling generative and predictive tasks, opening up new possibilities and aiming to lay the foundation for further exploration and development of graph-based generative AI models that can effectively incorporate behavior data for enhanced scene generation and synthesis.

Manos Kamarianakis, Nick Lydatakis, Antonis Protopsaltis et al.

In this work, we present an integrated geometric framework: "deep- cut" that enables for the first time a user to geometrically and algorithmically cut, tear and drill the surface of a skinned model without prior constraints, layered on top of a custom soft body mesh deformation algorithm. Both layered algorithms in this frame- work yield real-time results and are amenable for mobile Virtual Reality, in order to be utilized in a variety of interactive application scenarios. Our framework dramatically improves real-time user experience and task performance in VR, without pre-calculated or artificially designed cuts, tears, drills or surface deformations via predefined rigged animations, which is the current state-of-the-art in mobile VR. Thus our framework improves user experience on one hand, on the other hand saves both time and costs from expensive, manual, labour-intensive design pre-calculation stages.

George Papagiannakis, Paul Zikas, Nick Lydatakis et al.

In this work, we present MAGES 3.0, a novel Virtual Reality (VR)-based authoring SDK platform for accelerated surgical training and assessment. The MAGES Software Development Kit (SDK) allows code-free prototyping of any VR psychomotor simulation of medical operations by medical professionals, who urgently need a tool to solve the issue of outdated medical training. Our platform encapsulates the following novel algorithmic techniques: a) collaborative networking layer with Geometric Algebra (GA) interpolation engine b) supervised machine learning analytics module for real-time recommendations and user profiling c) GA deformable cutting and tearing algorithm d) on-the-go configurable soft body simulation for deformable surfaces.

Stéphanie Bertrand, Martha Vassiliadi, Paul Zikas et al.

This chapter will extend its preliminary scope by examining how literary transportation further amplifies presence and affects user response vis-á-vis virtual heritage by focusing on embodiment and aesthetic experience. To do so, it will draw on recent findings emerging from the fields of applied psychology, neuroaesthetics and cognitive literary studies; and consider a case study advancing the use of literary travel narratives in the design of DCH applications for Antiquities - in this case the well-known ancient Greek monument of Acropolis. Subsequently, the chapter will discuss how Literary-based MR Presence shifts public reception from an education-entertainment-touristic consumption paradigm to a response predicated on valuation. It will show that this type of public engagement is more closely aligned both with MR applications' default mode of usership, and with newly emerging conceptions of a user-centered museum (e.g., the Museum 3.0), thus providing a Virtual Museum model expressly suited to cultural heritage.