mlsanityData to 7 Oct 2026

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

arXiv 2306.10621 ↗First posted 18 Jun 2023cs.GR cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 19 references that are in the map. First posted on arXiv 18 Jun 2023.

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8 of 19 references.

References · 8

  1. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  2. 2019Strategies for Pre-training Graph Neural Networkscited by 964
  3. 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
  4. 2021Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphscited by 83
  5. 2022MAGES 4.0: Accelerating the world's transition to VR training and democratizing the authoring of the medical metaversecited by 4
  6. 2022Less Is More: Efficient Networked VR Transformation Handling Using Geometric Algebracited by 1
  7. 2022Recording and replaying psychomotor user actions in VRcited by 1
  8. 2023Project Elements: A computational entity-component-system in a scene-graph pythonic framework, for a neural, geometric computer graphics curriculumcited by 1

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