Deep Geometric Texture Synthesis is an approach for learning the local geometric textures present within a 3D mesh model. This can be used to learn the unknown 3D geometric texture statistics from a single 3D model, and then synthesizes them on different 3D models. This repository contains the code for:
(1) creating multi-scale training data
(2) training a series of multi-scale generators
(3) synthesizing the learned geometric textures on unseen models
- 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.shThe 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.shwill 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.shNote 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.shThis 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.shFirst get some example 3D meshes with geometric texture
bash ./scripts/gt_optimization/get_demo_data.shThe example scripts can be found in scripts/gt_optimization. For example, run the following from the root directory
bash ./scripts/gt_optimization/covid.shwhich 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.