## [Project page](https://PKU-YuanGroup.github.io/repaint123/) | [Paper](https://arxiv.org/abs/2312.13271) | [Live Demo (Coming Soon)]()
## 😮 Highlights
Repaint123 crafts 3D content from a single image, matching 2D generation quality in just ***2 minutes***.
### 🔥 Simple Gaussian Splatting baseline for image-to-3D
- Coarse stage: Gaussian Splatting optimized with SDS loss by Zero123 for geometry formation.
- Fine stage: Mesh optimized with MSE loss by Stable Diffusion for texture refinement.
### 💡 View consistent, high quality and fast speed
- Stable Diffusion for high quality and controllable repainting for reference alignment --> view-consistent high-quality image generation.
- View-consistent high-quality images with simple MSE loss --> fast high-quality 3D content reconstruction.
## 🚩 **Updates**
Welcome to **watch** 👀 this repository for the latest updates.
✅ **[2023.12.21]** : We have released our paper, Repaint123 on [arXiv](https://arxiv.org/abs/2312.13271).
✅ **[2023.12.21]** : Release [project page](https://PKU-YuanGroup.github.io/repaint123/).
- [ ] Code release.
- [ ] Online Demo.
## 🤗 Demo
Coming soon!
## 🚀 Image-to-3D Results
### Qualitative comparison
### Quantitative comparison
## 👍 **Acknowledgement**
This work is built on many amazing research works and open-source projects, thanks a lot to all the authors for sharing!
* [DreamGaussian](https://github.com/dreamgaussian/dreamgaussian)
* [ControlNet](https://github.com/lllyasviel/ControlNet)
* [Repaint](https://github.com/andreas128/RePaint)
* [Diffusers](https://github.com/huggingface/diffusers)
## ✏️ Citation
If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.
```BibTeX
@misc{zhang2023repaint123,
title={Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable 2D Repainting},
author={Junwu Zhang and Zhenyu Tang and Yatian Pang and Xinhua Cheng and Peng Jin and Yida Wei and Wangbo Yu and Munan Ning and Li Yuan},
year={2023},
eprint={2312.13271},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## 😮 Highlights
Repaint123 crafts 3D content from a single image, matching 2D generation quality in just ***2 minutes***.
### 🔥 Simple Gaussian Splatting baseline for image-to-3D
- Coarse stage: Gaussian Splatting optimized with SDS loss by Zero123 for geometry formation.
- Fine stage: Mesh optimized with MSE loss by Stable Diffusion for texture refinement.
### 💡 View consistent, high quality and fast speed
- Stable Diffusion for high quality and controllable repainting for reference alignment --> view-consistent high-quality image generation.
- View-consistent high-quality images with simple MSE loss --> fast high-quality 3D content reconstruction.
## 🚩 **Updates**
Welcome to **watch** 👀 this repository for the latest updates.
✅ **[2023.12.21]** : We have released our paper, Repaint123 on [arXiv](https://arxiv.org/abs/2312.13271).
✅ **[2023.12.21]** : Release [project page](https://PKU-YuanGroup.github.io/repaint123/).
- [ ] Code release.
- [ ] Online Demo.
## 🤗 Demo
Coming soon!
## 🚀 Image-to-3D Results
### Qualitative comparison
### Quantitative comparison
## 👍 **Acknowledgement**
This work is built on many amazing research works and open-source projects, thanks a lot to all the authors for sharing!
* [DreamGaussian](https://github.com/dreamgaussian/dreamgaussian)
* [ControlNet](https://github.com/lllyasviel/ControlNet)
* [Repaint](https://github.com/andreas128/RePaint)
* [Diffusers](https://github.com/huggingface/diffusers)
## ✏️ Citation
If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.
```BibTeX
@misc{zhang2023repaint123,
title={Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable 2D Repainting},
author={Junwu Zhang and Zhenyu Tang and Yatian Pang and Xinhua Cheng and Peng Jin and Yida Wei and Wangbo Yu and Munan Ning and Li Yuan},
year={2023},
eprint={2312.13271},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```