中文版本 中文
Official implementation of Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image
Kailu Wu, Fangfu Liu, Zhihan Cai, Runjie Yan, Hanyang Wang, Yating Hu, Yueqi Duan, Kaisheng Ma
High-fidelity and diverse textured meshes generated by Unique3D from single-view wild images in 30 seconds.
The repo is still being under construction, thanks for your patience.
- Local gradio demo.
- Detailed tutorial.
- Huggingface demo.
- Detailed local demo.
- Comfyui support.
- Windows support.
- Docker support.
- More stable reconstruction with normal.
- Training code release.
conda create -n unique3d
conda activate unique3d
pip install -r requirements.txt
- Download the ckpt.zip, and extract it to
ckpt/*.
Unique3D
├──ckpt
├── controlnet-tile/
├── image2normal/
├── img2mvimg/
├── realesrgan-x4.onnx
└── v1-inference.yaml
- Run the interactive inference locally.
python app/gradio_local.py --port 7860- Unique3D is sensitive to the facing direction of input images. Due to the distribution of the training data, orthographic front-facing images with a rest pose always lead to good reconstructions.
- Images with occlusions will cause worse reconstructions, since four views cannot cover the complete object. Images with fewer occlusions lead to better results.
- Pass an image with as high a resolution as possible to the input when resolution is a factor.
We have intensively borrowed code from the following repositories. Many thanks to the authors for sharing their code.
Our mission is to create a 4D generative model with 3D concepts. This is just our first step, and the road ahead is still long, but we are confident. We warmly invite you to join the discussion and explore potential collaborations in any capacity. If you're interested in connecting or partnering with us, please don't hesitate to reach out via email ([email protected]).