4 citations · 5 across the 6 of their papers we have counts for
6 papers
Geometric Algebra Meets Large Language Models: Instruction-Based Transformations of Separate Meshes in 3D, Interactive and Controllable Scenes
Prodromos Kolyvakis, Manos Kamarianakis, George Papagiannakis
This paper introduces a novel integration of Large Language Models (LLMs) with Conformal Geometric Algebra (CGA) to revolutionize controllable 3D scene editing, particularly for ob…
UniSG^GA: A 3D scenegraph powered by Geometric Algebra unifying geometry, behavior and GNNs towards generative AI
Manos Kamarianakis, Antonis Protopsaltis, Dimitris Angelis +3
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…
Project Elements: A computational entity-component-system in a scene-graph pythonic framework, for a neural, geometric computer graphics curriculum
George Papagiannakis, Manos Kamarianakis, Antonis Protopsaltis +2
We present the Elements project, a lightweight, open-source, computational science and computer graphics (CG) framework, tailored for educational needs, that offers, for the first…
Progressive tearing and cutting of soft-bodies in high-performance virtual reality
Manos Kamarianakis, Antonis Protopsaltis, Dimitris Angelis +2
We present an algorithm that allows a user within a virtual environment to perform real-time unconstrained cuts or consecutive tears, i.e., progressive, continuous fractures on a d…
MAGES 4.0: Accelerating the world's transition to VR training and democratizing the authoring of the medical metaverse
Paul Zikas, Antonis Protopsaltis, Nick Lydatakis +14
In this work, we propose MAGES 4.0, a novel Software Development Kit (SDK) to accelerate the creation of collaborative medical training applications in VR/AR. Our solution is essen…
Assessing unconstrained surgical cuttings in VR using CNNs
Ilias Chrysovergis, Manos Kamarianakis, Mike Kentros +3
We present a Convolutional Neural Network (CNN) suitable to assess unconstrained surgical cuttings, trained on a dataset created with a data augmentation technique.