| title | BlenderRAG: High-Fidelity 3D Object Generation via Retrieval-Augmented Code Synthesis |
|---|---|
| authors | Massimo Rondelli, Francesco Pivi, Maurizio Gabbrielli |
| date | 2026-05-01 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.00632 |
| affiliation | University of Bologna |
| links | abs pdf hf code |
| abstract | Automatic generation of executable Blender code from natural language remains challenging, with state-of-the-art LLMs producing frequent syntactic errors and geometrically inconsistent objects. We present BlenderRAG, a retrieval-augmented generation system that operates on a curated multimodal dataset of 500 expert-validated examples (text, code, image) across 50 object categories. By retrieving semantically similar examples during generation, BlenderRAG improves compilation success rates from 40.8% to 70.0% and semantic normalized alignment from 0.41 to 0.77 (CLIP similarity) across four state-of-the-art LLMs, without requiring fine-tuning or specialized hardware, making it immediately accessible for deployment. The dataset and code will be available at https://github.com/MaxRondelli/BlenderRAG. |
cite
@misc{rondelli2026blenderrag,
title={BlenderRAG: High-Fidelity 3D Object Generation via Retrieval-Augmented Code Synthesis},
author={Massimo Rondelli and Francesco Pivi and Maurizio Gabbrielli},
year={2026},
eprint={2605.00632},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.00632}
}