UniSG^GA: A 3D scenegraph powered by Geometric Algebra unifying
  geometry, behavior and GNNs towards generative AI
Manos Kamarianakis Antonis Protopsaltis Dimitris Angelis Paul Zikas Mike Kentros George Papagiannakis
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.