Update README.md

README.md

@@ -5,4 +5,48 @@ license_link: LICENSE
 datasets:
 - Major-TOM/Core-S2L2A
 - Major-TOM/Core-DEM
----
+---
+
+<h1 align="center">MESA: Text-Driven Terrain Generation Using Latent Diffusion and Global Copernicus Data </h1>
+<p align="center"><a href="https://www.linkedin.com/in/paul-bp-cs/" target="_blank">Paul Borne--Pons</a>, <a href="https://mikonvergence.github.io/" target="_blank">Mikolaj Czerkawski</a>,<a href="https://research.adobe.com/person/rosalie-martin/" target="_blank">Rosalie Martin</a>,
+<a href="https://research.adobe.com/person/romain-rouffet/" target="_blank">Romain Rouffet</a></p>
+
+<p align="center"><a href="https://sites.google.com/view/morse2025" target="_blank">CVPR 2025 Workshop MORSE</a> </p>
+<p align="center"><img src=assets/mesa-header-nz.png></p>
+
+MESA is a novel generative model based on latent denoising diffusion capable of generating 2.5D representations of terrain based on the text prompt conditioning supplied via natural language. The model produces two co-registered modalities of optical and depth maps.
+
+## Model Description
+- **Paper:** [MESA: Text-Driven Terrain Generation Using Latent Diffusion and Global Copernicus Data](https://arxiv.org/abs/2504.07210)
+- **Github:** <https://github.com/PaulBorneP/MESA>
+- **Project page:** 
+- **License:** [Adobe License](https://huggingface.co/NewtNewt)
+
+## Installation
+```sh
+# Clone the repository
+git clone https://github.com/PaulBorneP/MESA.git
+cd MESA
+```
+
+## Model Download
+
+```sh
+huggingface-cli download NewtNewt/MESA --local-dir ./weights
+```
+
+```latex
+@inproceedings{mesa2025,
+title={MESA: Text-Driven Terrain Generation Using Latent Diffusion and Global Copernicus Data},
+author={Paul Borne--Pons and Mikolaj Czerkawski and Rosalie Martin and Romain Rouffet},
+year={2025},
+booktitle={MORSE Workshop at CVPR 2025},
+eprint={2504.07210},
+url={https://arxiv.org/abs/2504.07210},}
+```
+
+## Acknowledgements
+
+This implementation builds upon Hugging Face’s [Diffusers](https://github.com/huggingface/diffusers) library. We also acknowledge [Gradio](https://www.gradio.app/) for providing an easy-to-use interface that allowed us to create the inference demos for our models.
+
+This model is the product of a collaboration between [Φ-lab, European Space Agency (ESA)](https://philab.esa.int/) and the [Adobe Research (Paris, France)](https://research.adobe.com/careers/paris/).