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 +1 more
6/18/2023
cs.GRcs.LG

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Abstract

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.

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Cite this paper

@article{kamarianakis2023unisgga,
  title  = {UniSG^GA: A 3D scenegraph powered by Geometric Algebra unifying geometry, behavior and GNNs towards generative AI},
  author = {Manos Kamarianakis and Antonis Protopsaltis and Dimitris Angelis and Paul Zikas and Mike Kentros and George Papagiannakis},
  year   = {2023},
  eprint = {2306.10621},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.10621v1}
}

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