arxiv:2605.00632

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

Published on May 1

· Submitted by

Francesco Pivi on May 5

University of Bologna

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Authors:

Massimo Rondelli ,

Francesco Pivi ,

Abstract

BlenderRAG enhances natural language to Blender code generation by leveraging a retrieval-augmented approach with a curated multimodal dataset, improving both compilation success and semantic alignment without fine-tuning.

AI-generated summary

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.