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This work presents the introduction of UniSG^GA, a novel integratedscenegraph structure, that to incorporates behavior and geometry data on a 3Dscene. It is specifically designed to seamlessly integrate Graph NeuralNetworks (GNNs) and address the challenges associated with transforming a 3Dscenegraph (3D-SG) during generative tasks. To effectively capture and preservethe topological relationships between objects in a simplified way, within thegraph representation, we propose UniSG^GA, that seamlessly integrates GeometricAlgebra (GA) forms. This novel approach enhances the overall performance andcapability of GNNs in handling generative and predictive tasks, opening up newpossibilities and aiming to lay the foundation for further exploration anddevelopment of graph-based generative AI models that can effectivelyincorporate behavior data for enhanced scene generation and synthesis.

UniSG^GA: A 3D scenegraph powered by Geometric Algebra unifying geometry, behavior and GNNs towards generative AI