A Framework for Procedural Generation and Evaluation of 3D Assets Driven by Large Language Models

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Key Points

The aim is to develop a method for automated 3D content generation driven by natural language instructions, enhancing quality and control.
Translating natural language descriptions into script commands for the InfiniGen library to create 3D models.
Employing the CLIP model for filtering and optimizing the generated 3D assets.
Automatically selecting 3D models based on evaluation scores.
The proposed approach significantly improves the quality of generated 3D models compared to baseline methods without CLIP filtering.
Higher CLIP matching scores achieved across multiple categories of 3D object generation tasks.
Demonstrates the feasibility of combining large models with procedural 3D generation techniques.

Demonstrates improved 3D asset generation in various categories, suggesting effective language-driven techniques.

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Hang et al. (2026) studied this question.

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