Learning to Evolve Scenes: Reasoning about Human Activities with Scene Graphs

Francesca Pistilli • Simone Alberto Peirone • Giuseppe Averta

Politecnico di Torino

TL;DR: Code for GLEN (Graph-Language Edit Network), a graph neural network trained for graph-language alignment and activity-conditioned graph edits predictions to model scene evolution. Code coming soon.

Acknowledgements

This study was carried out within the FAIR - Future Artificial Intelligence Research and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.3 – D.D. 1555 11/10/2022, PE00000013). This manuscript reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible for them. We acknowledge the CINECA award under the ISCRA initiative, for the availability of high performance computing resources and support.

Cite Us

@article{pistilli2026glen,
  title={Learning to Evolve Scenes: Reasoning about Human Activities with Scene Graphs},
  author={Pistilli, Francesca and Peirone, Simone Alberto and Averta, Giuseppe},
  journal={arXiv preprint arXiv:2607.02425},
  year={2026}
}