• Clone this repo git clone https://github.com/amirhertz/geometric-textures.git.
• Install via conda environment conda env create -f environment.yml (creates an environment called dgts)

We provide an End-to-End Notebook which covers the 3 steps above. Or you can run each step seperately below. To use the Jupyter demo you should additionally install: jupyter, requests, and pytorch-gpu

First get the multi-scaled training inputs already prepared by running

bash ./scripts/train/get_train_data.sh

The example scripts can be found in scripts/train. If using conda env first activate env e.g. conda activate dgts, then from the root directory:

bash ./scripts/train/virus_ball.sh

will train on the spikey-ball from the paper. There is also a demo script for the "sphere rail" and the lizard.

bash ./scripts/inference/get_pretrained_data.sh

Note that if you already ran the training demo from above, this will overwrite some your training snapshots.

This will generate unconditional & conditioned on coarse mesh generative results (Fig. 5 from the paper):

bash ./scripts/inference/anky_generate.sh

This will generate a series of progressive textures. The target mesh will progressively gain textures, starting from a low-level, generator, up to a finer resolution generator. This results in a series of animated textures.

bash ./scripts/inference/sphere_rail_animate.sh

First get some example 3D meshes with geometric texture

bash ./scripts/gt_optimization/get_demo_data.sh

The example scripts can be found in scripts/gt_optimization. For example, run the following from the root directory

bash ./scripts/gt_optimization/covid.sh

which will generate the coronavirus from the paper.

If you find this code useful, please consider citing our paper

@article{Hertz2020deep,
  title = {Deep Geometric Texture Synthesis},
  author = {Hertz, Amir and Hanocka, Rana and Giryes, Raja and Cohen-Or, Daniel},
  year = {2020},
  issue_date = {July 2020}, 
  publisher = {Association for Computing Machinery}, 
  volume = {39}, 
  number = {4}, 
  issn = {0730-0301},
  url = {https://doi.org/10.1145/3386569.3392471},
  doi = {10.1145/3386569.3392471},
  articleno = {108},
  journal = {ACM Trans. Graph.} 
}

If you have questions or issues running this code, please open an issue.