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Unique3D

High-Quality and Efficient 3D Mesh Generation from a Single Image

Paper | Project page | Huggingface Demo | Online Demo

High-fidelity and diverse textured meshes generated by Unique3D from single-view wild images in 30 seconds.

More features

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.

Preparation for inference

Linux System Setup.

conda create -n unique3d
conda activate unique3d
pip install -r requirements.txt

Interactive inference: run your local gradio demo.

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

Tips to get better results

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.

Acknowledgement

We have intensively borrowed code from the following repositories. Many thanks to the authors for sharing their code.

Collaborations

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]).