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}
}