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--- |
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license: openrail |
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language: |
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- en |
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pipeline_tag: image-to-image |
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datasets: |
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- mvp18/gscenes_pretrain |
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tags: |
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- diffusion |
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- image-to-image |
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- image-to-3d |
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- 3d-reconstruction |
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- gaussian-splatting |
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- pose-free |
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- sparse-view |
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- rgbd |
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base_model: |
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- stabilityai/stable-diffusion-2 |
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--- |
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## Summary |
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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: |
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- **UNet** — denoising backbone (Diffusers UNet) adapted for our pipeline. |
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- **VAE** — variational autoencoder used for latent encoding/decoding. |
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These checkpoints are intended for research use and model reproducibility. |
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## Usage |
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For a guide on how to use this model, check out the [official repository](https://github.com/gaussian-scenes). |
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## Citation |
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If you use these checkpoints in your work, please cite the associated paper: |
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``` |
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@article{ |
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paul2025gaussian, |
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title={Gaussian Scenes: Pose-Free Sparse-View Scene Reconstruction using Depth-Enhanced Diffusion Priors}, |
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author={Soumava Paul and Prakhar Kaushik and Alan Yuille}, |
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journal={Transactions on Machine Learning Research}, |
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issn={2835-8856}, |
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year={2025}, |
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url={https://openreview.net/forum?id=yp1CYo6R0r}, |
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note={} |
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} |
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``` |
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The HuggingFace paper page can be found [here](https://huggingface.co/papers/2411.15966). |
