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README.md

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-license: mit
-pipeline_tag: image-to-3d
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-
-# LGM Full
-
-This custom pipeline encapsulates the full [LGM](https://huggingface.co/ashawkey/LGM) pipeline, including [multi-view diffusion](https://huggingface.co/ashawkey/imagedream-ipmv-diffusers).
-
-It is provided as a resource for the [ML for 3D Course](https://huggingface.co/learn/ml-for-3d-course).
-
-Original LGM paper: [LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation](https://huggingface.co/papers/2402.05054).
+---
+license: mit
+pipeline_tag: image-to-3d
+---
+
+# LGM Full
+
+# NLP to 3D Model – Custom Pipeline
+
+## 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](https://huggingface.co/papers/2402.05054)**, 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](https://arxiv.org/abs/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