---
license: openrail
language:
- en
pipeline_tag: image-to-image
datasets:
  - mvp18/gscenes_pretrain
tags:
  - diffusion
  - image-to-image
  - image-to-3d
  - 3d-reconstruction
  - gaussian-splatting
  - pose-free
  - sparse-view
  - rgbd
base_model:
  - stabilityai/stable-diffusion-2
---

## Summary

This repository provides checkpoints used in the **Gaussian Scenes** pipeline for pose-free, sparse-view scene reconstruction. The weights are stored in Diffusers format and organized as two components:

- **UNet** — denoising backbone (Diffusers UNet) adapted for our pipeline.
- **VAE** — variational autoencoder used for latent encoding/decoding.

These checkpoints are intended for research use and model reproducibility.

## Usage

For a guide on how to use this model, check out the [official repository](https://github.com/gaussian-scenes).

## Citation

If you use these checkpoints in your work, please cite the associated paper:

```
@article{
paul2025gaussian,
title={Gaussian Scenes: Pose-Free Sparse-View Scene Reconstruction using Depth-Enhanced Diffusion Priors},
author={Soumava Paul and Prakhar Kaushik and Alan Yuille},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2025},
url={https://openreview.net/forum?id=yp1CYo6R0r},
note={}
}
```

The HuggingFace paper page can be found [here](https://huggingface.co/papers/2411.15966).