Open-source Tools for Images - 3D Models: Generate 3D Models Effortlessly with Natural Language

Images 3Dmodel

Developed by gordon-0115

An experimental natural language to 3D model generation pipeline based on an improved pre-trained multi-view diffusion model

Text-to-Image Open Source License:MIT #Text-to-3D Generation #Multi-view Diffusion #Natural Language Modeling

Downloads 20

Release Time : 5/2/2025

Model Overview

This project achieves a pipeline from natural language descriptions to 3D model generation by improving pre-trained multi-view diffusion models, primarily exploring the potential of AI-assisted 3D content creation using natural language input.

Model Features

Natural Language to 3D Conversion

Supports natural language input, internally maps text to representative images or multi-view descriptions, and ultimately generates 3D models

Academic Research-Oriented

As a graduation project, it primarily explores the potential of AI-assisted 3D content creation

Based on Pre-trained Models

Developed based on the LGM model by Google Research team, not an original architecture

Model Capabilities

Natural Language Understanding

3D Model Generation

Multi-view Image Generation

Use Cases

3D Content Creation

Rapid Prototyping

Quickly generate 3D model prototypes through natural language descriptions

Simplifies the 3D modeling process

Educational Demonstration

Used to showcase the conversion process from natural language to 3D models

Intuitively demonstrates AI-assisted creation technology

🚀 LGM Full

A custom pipeline for generating 3D models from natural language prompts

🚀 Quick Start

This README provides an overview of the project that showcases an experimental pipeline for generating 3D models from natural language prompts.

✨ Features

Bridges natural language prompts to 3D model generation.
Utilizes a modified version of a pre - trained multi - view diffusion model.
Allows input of a natural language description, maps it internally, and generates a 3D model using the LGM pipeline.

📚 Documentation

Overview

This project showcases an experimental pipeline that bridges natural language prompts to 3D model generation using a modified version of a pre - trained multi - view diffusion model.

It is part of a final year project for the Comprehensive Creative Technologies Project at UWE Bristol. The primary aim was to explore the potential of AI - assisted 3D content creation using natural language input.

Model Source & Attribution

This project relies on the pre - trained model from the LGM: Large Multi - View Gaussian Model for High - Resolution 3D Content Creation, developed for the ML for 3D Course by researchers at Google Research.

📌 Original Model: [https://huggingface.co/spaces/dylanebert/LGM - tiny]
📄 Paper: arXiv:2402.05054
📄 License: MIT

I do not claim authorship of the model architecture or training process. This space serves as a custom wrapper for experimentation with text - to - 3D workflows.

What This Model Does

Allows input of a natural language description
Internally maps the input to a representative image or multi - view description
Generates a 3D model using the LGM pipeline

Limitations

This is a prototype for academic use only.
The model's ability to handle complex or abstract text is limited.
Performance and quality depend entirely on the base pre - trained model.

Acknowledgements

Thanks to Hugging Face and the authors of LGM for making their models publicly available.

Author

Gordon CHIN HO AU
Final Year BSc Digital Media
University of the West of England, Bristol

📄 License

This project is licensed under the MIT license.

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