BlenderRAG: High-Fidelity 3D Object Generation via Retrieval-Augmented Code Synthesis

Massimo Rondelli, Francesco Pivi, Maurizio Gabbrielli

BlenderRAG: High-Fidelity 3D Object Generation via Retrieval-Augmented Code Synthesis: 0 upvotes on Hugging Face Daily Papers, #24 of 25 papers on 2026-05-05. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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