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

arXiv: https://arxiv.org/abs/2306.10621 · HTML: https://mlsanity.com/paper/2306.10621/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 18 Jun 2023.

## Builds on (most cited first, 8 of 8)

- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generation](https://mlsanity.com/paper/1802.05384/) (2018)
- [Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs](https://mlsanity.com/paper/2108.08841/) (2021)
- [MAGES 4.0: Accelerating the world's transition to VR training and democratizing the authoring of the medical metaverse](https://mlsanity.com/paper/2209.08819/) (2022)
- [Less Is More: Efficient Networked VR Transformation Handling Using Geometric Algebra](https://mlsanity.com/paper/2203.10988/) (2022)
- [Recording and replaying psychomotor user actions in VR](https://mlsanity.com/paper/2205.00923/) (2022)
- [Project Elements: A computational entity-component-system in a scene-graph pythonic framework, for a neural, geometric computer graphics curriculum](https://mlsanity.com/paper/2302.07691/) (2023)

## 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.