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Texture generation using a Graph Generative Adversarial Network and Differentiable Rendering

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Source code for the paper:

Texture Generation Using a Graph Generative Adversarial Network and Differentiable Rendering

https://link.springer.com/chapter/10.1007/978-3-031-25825-1_28
    
https://arxiv.org/pdf/2206.08547.pdf

Download ShapeNet car dataset from: https://shapenet.org/

requirements:

python==3.8
    pytorch==1.9.0+cu111
    pytorch3d==0.6.0

train [ggan model]:

sh Experiments/gnn_kraken.sh

test [ggan model]:

sh Experiments/test.sh

Note: The source code contains multiple files used to train other models

Note: The optimization problem is complicated [changing hyperparameters slightly may result in a huge change in the generated texture quality]

If you use the source code please cite the following paper:

@inproceedings{dharma2023texture,
      title={Texture Generation Using a Graph Generative Adversarial Network and Differentiable Rendering},
      author={Dharma, KC and Morrison, Clayton T and Walls, Bradley},
      booktitle={Image and Vision Computing: 37th International Conference, IVCNZ 2022, Auckland, New Zealand, November 24--25, 2022, Revised Selected Papers},
      pages={388--401},
      year={2023},
      organization={Springer}
    }

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