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## 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 the checkpoints, check out the [https://github.com/mvp18/gaussian-scenes](official repository).

## 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 [https://huggingface.co/papers/2411.15966](here).

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+ ---
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+ license: openrail
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+ language:
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+ - en
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+ base_model:
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+ - stabilityai/stable-diffusion-2-1
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+ pipeline_tag: image-to-3d
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+ ---