Wonder3D

Single Image to 3D using Cross-Domain Diffusion

Paper | Project page | Hugging Face Demo | Colab from @camenduru

Wonder3D reconstructs highly-detailed textured meshes from a single-view image in only 2 ∼ 3 minutes. Wonder3D first generates consistent multi-view normal maps with corresponding color images via a cross-domain diffusion model, and then leverages a novel normal fusion method to achieve fast and high-quality reconstruction.

Schedule

• Inference code and pretrained models.
• Huggingface demo.
• New model trained on the whole Objaverse dataset.

Preparation for inference

1. Install packages in requirements.txt.

conda create -n wonder3d
conda activate wonder3d
pip install -r requirements.txt

Install tiny-cuda-nn PyTorch extension for mesh extraction: pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch

2. Download the checkpoints and into the root folder.

Inference

1. Make sure you have the following models.

Wonder3D
|-- ckpts
    |-- unet
    |-- scheduler.bin
    ...

2. Predict foreground mask as the alpha channel. We use Clipdrop to segment the foreground object interactively. You may also use rembg to remove the backgrounds.

# !pip install rembg
import rembg
result = rembg.remove(result)
result.show()

3. Run Wonder3d to produce multiview-consistent normal maps and color images. Then you can check the results in the folder ./outputs. (we use rembg to remove backgrounds of the results, but the segmemtations are not always perfect.)

accelerate launch --config_file 1gpu.yaml test_mvdiffusion_seq.py \
            --config mvdiffusion-joint-ortho-6views.yaml

or

bash run_test.sh

4. Mesh Extraction

cd ./instant-nsr-pl
bash run.sh output_folder_path scene_name

Our generated normals and color images are defined in orthographic views, so the reconstructed mesh is also in orthographic camera space. If you use MeshLab to view the meshes, you can click Toggle Orthographic Camera in View tab.

Citation

If you find this repository useful in your project, please cite the following work. :)

@misc{long2023wonder3d,
      title={Wonder3D: Single Image to 3D using Cross-Domain Diffusion}, 
      author={Xiaoxiao Long and Yuan-Chen Guo and Cheng Lin and Yuan Liu and Zhiyang Dou and Lingjie Liu and Yuexin Ma and Song-Hai Zhang and Marc Habermann and Christian Theobalt and Wenping Wang},
      year={2023},
      eprint={2310.15008},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}