/GRM

Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation

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GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation

GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation
Yinghao Xu*, Zifan Shi*, Wang Yifan, Hansheng Chen, Ceyuan Yang, Sida Peng, Yujun Shen, Gordon Wetzstein

[Paper] [Project Page] [Blender Demo] [HF Demo][Weights]

3d-generation.mp4

Todo List

  • Release gradio demo code.
  • Release inference code.
  • Release pretrained models.
  • Release training code.

GRM Demo

Requirements

  • 64-bit Python 3.10 and PyTorch 2.0.1 or higher.
  • CUDA 11.8
  • Users can use the following commands to install the packages
conda create -n grm python=3.10
conda activate grm 
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu118
cd third_party/diff-gaussian-rasterization &&  pip install -e .

Pretrained weights

Pretrained weights can be downloaded from Hugging Face.

# Example
mkdir checkpoints && cd checkpoints
wget https://huggingface.co/justimyhxu/GRM/blob/main/grm_u.pth && cd ..

Note that we provide three checkpoints for use. We use the OpenCV coordinate system.

Checkpoint Training settings
grm_u.pth The elevations are all 20 degrees and the azimuths uniformly cover all the 360-degree information.
grm_r.pth The azimuths roughly cover the 360-degree information.
grm_zero123plus.pth Three views are with 30-degree elevations and the azimuths are evenly distributed at intervals of 120 degrees. Another view has the elevation of -20 degrees and the azimuth is 60 degrees different from one of the three.
instant3d.pth We reproduce the first-stage diffusion model of instant3d, which can produce consistent multi-view images.

Besides, you need to download checkpoints for SV3D.

cd checkpoints
wget https://huggingface.co/stabilityai/sv3d/blob/main/sv3d_p.safetensors && cd ..

Inference

# text-to-3D
python test.py --prompt 'a car made out of cheese'
# image-to-3D with zero123plus-v1.1
python test.py --image_path examples/dragon2.png --model zero123plus-v1.1
# image-to-3D with zero123plus-v1.2
python test.py --image_path examples/dragon2.png --model zero123plus-v1.2
# image-to-3D with SV3D
python test.py --image_path examples/dragon2.png --model sv3d

Add --fuse_mesh True if you would like to get the textured mesh. Add --optimize_texture True if you would like to optimize texture on extracted textured mesh.

Gradio Demo

We provide an offline gradio demo, which can be run with the following command:

python app.py

Results

Blender Demo

blender_demo.mp4

Sparse-view Reconstruction

sparse-view.mp4

Acknowledgement

We thank all of the following amazing codes:

BibTeX

@article{xu2024grm,
     author    = {Xu, Yinghao and Shi, Zifan and Yifan, Wang and Peng, Sida and Yang, Ceyuan and Shen, Yujun and Wetzstein Gordon},
     title     = {GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation},
     journal   = {arxiv: 2403.14621},
     year      = {2024},
    }