Geometry-Informed Neural Networks
<|reference_start|>Geometry-Informed Neural Networks: Geometry is a ubiquitous language of computer graphics, design, and engineering. However, the lack of large shape datasets limits the application of state-of-the-art supervised learning methods and motivates the exploration of alternative learning strategies. To this end, we introduce geometry-informed neural networks (GINNs) to train shape generative models \emph{without any data}. GINNs combine (i) learning under constraints, (ii) neural fields as a suitable representation, and (iii) generating diverse solutions to under-determined problems. We apply GINNs to several two and three-dimensional problems of increasing levels of complexity. Our results demonstrate the feasibility of training shape generative models in a data-free setting. This new paradigm opens several exciting research directions, expanding the application of generative models into domains where data is sparse.<|reference_end|>
@article{berzins2024geometry-informed,
    title={Geometry-Informed Neural Networks},
    author={Arturs Berzins, Andreas Radler, Eric Volkmann, Sebastian Sanokowski,
  Sepp Hochreiter, Johannes Brandstetter},
    journal={arXiv preprint arXiv:2402.14009},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14009},
    primaryClass={cs.LG cs.CV}
}