Bootstrap3D

Bootstrap3D: Improving 3D Content Creation with Synthetic Data
Zeyi Sun, Tong Wu, Pan Zhang, Yuhang Zang, Xiaoyi Dong Yuanjun Xiong, Dahua Lin, Jiaqi Wang

📜 News

🚀 [2024/6/4] The paper and project page are released!

💡 Highlights

• 🔥 A new Multi-View Diffusion model trained on high quality synthetic data and capable of generating multi-view images closely follow text prompt.
• 🔥 Denser captioned Objaverse Dataset using finetuned 3D aware MV-LLaVA powered by GPT-4V.
• 🔥 A High Quality synthetic dataset for high asethetic 3D content creation.

👨‍💻 Todo

• Training code of MV-Diffusion model based on PixArt.
• BS-Synthetic3D HQ 3D-object dataset.
• Release of MV-PixArt-alpha, MV-Pixart-sigma model
• BS-Objaverse Dataset cart launched on huggingface.
• MV-LLaVA model and web demo.
• Paper and project page.

⚡ Quick Start

✒️ Citation

If you find our work helpful for your research, please consider giving a star ⭐ and citation 📝

@misc{sun2024bootstrap3d,
      title={Bootstrap3D: Improving 3D Content Creation with Synthetic Data}, 
      author={Zeyi Sun and Tong Wu and Pan Zhang and Yuhang Zang and Xiaoyi Dong and Yuanjun Xiong and Dahua Lin and Jiaqi Wang},
      year={2024},
      eprint={2406.00093},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}