Awesome
<h2 align="center"> <a href="https://github.com/PKU-YuanGroup/repaint123">Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable 2D Repainting</a></h2> <h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for latest update. </h2> <h5 align="center">
Project page | Paper | Live Demo (Coming Soon)
<img src="assets/teaser.png"/>😮 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.
✅ [2023.12.21] : Release project page.
- Code release.
- Online Demo.
🤗 Demo
Coming soon!
🚀 Image-to-3D Results
Qualitative comparison
<img src="assets/qual-comparison.jpg"/>Quantitative comparison
<img src="assets/quan-comparison.png"/>👍 Acknowledgement
This work is built on many amazing research works and open-source projects, thanks a lot to all the authors for sharing!
✏️ Citation
If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.
@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}
}