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https://github.com/henzler/neuraltexture
Learning a Neural 3D Texture Space from 2D Exemplars [CVPR 2020]
https://github.com/henzler/neuraltexture
computer-graphics computer-vision deep-learning machine-learning texture-synthesis
Last synced: 7 days ago
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Learning a Neural 3D Texture Space from 2D Exemplars [CVPR 2020]
- Host: GitHub
- URL: https://github.com/henzler/neuraltexture
- Owner: henzler
- License: mit
- Created: 2020-03-05T18:43:34.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-07-25T10:58:40.000Z (4 months ago)
- Last Synced: 2024-11-01T03:42:09.584Z (14 days ago)
- Topics: computer-graphics, computer-vision, deep-learning, machine-learning, texture-synthesis
- Language: Python
- Homepage: https://geometry.cs.ucl.ac.uk/projects/2020/neuraltexture/
- Size: 1.66 MB
- Stars: 108
- Watchers: 6
- Forks: 20
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Neural Texture
Official code repository for the paper:
**Learning a Neural 3D Texture Space from 2D Exemplars [CVPR, 2020]**
[Henzler](https://henzler.github.io), [J. Mitra](http://www0.cs.ucl.ac.uk/staff/n.mitra/), [Ritschel](http://www.homepages.ucl.ac.uk/~ucactri/)
**[[Paper](https://geometry.cs.ucl.ac.uk/projects/2020/neuraltexture/paper_docs/neuraltexture.pdf)] [[Project page](https://geometry.cs.ucl.ac.uk/projects/2020/neuraltexture/)]**
## Data
We downloaded all our textures from [https://www.textures.com/](https://www.textures.com/).
Due to licensing reasons we cannot provide the data for training, however, we provide pre-trained models under `trained_models` for the classes `wood, grass, marble, rust_paint`.
#### Inference
In order to evaluate textures, add the desired texture to the corresponding folder under `datasets//test` and use one of the pre-trained models under `trained_models/` and run the evaluation (see instructions below). We already provide some exemplars.
#### Training
For training you will need to provide data sets under `datasets/` and provide two subdirectories: `train` and `test`.
We provide `test` exemplars for `wood`, `grass`, `marble` and `rust_paint`. If you would like to train using these classes please add a `train` folder containing training data.
#####
## Prerequisites
- Ubuntu 18.04
- cuDNN 7
- CUDA 10.1
- python3+
- pyTorch 1.4
- Download pretrained models (optional)
### Install dependencies
```
cd code/
pip install -r requirements.txt
cd custom_ops/noise
# build cuda code for noise sampler
TORCH_CUDA_ARCH_LIST= python setup.py install
```
### Download pre-trained models
```
sh download_pretrained_models.sh
```
#### Logs
To visualise pre-trained training logs run the following:
```
tensorboard --logdir=./trained_models
```
## Usage
### Config file
The config files are located in `code/configs/neural_texture`. In the following we give an explanation for the
most important variables:
```
dim: 2 # choose between 2 and 3 for 2D and 3D.
dataset:
path: '../datasets/wood' # set path
use_single: -1 # -1 = train entire data set | 0,1,2,... = for single training
```
### Training
```
cd code/
python train_neural_texture.py --config_path= --job_id=
```
The default `config_path` is set to `configs/neural_texture/config_default.yaml`. The default `job_id` is set to `1`.
### Inference
```
cd code/
python test_neural_texture.py --trained_model_path=path/to/models
```
The default `trained_model_path` is set to `../trained_models`. The results are saved under `trained_model_path/{model}/results`
## Bibtex
If you use the code, please cite our paper:
```
@inproceedings{henzler2020neuraltexture,
title={Learning a Neural 3D Texture Space from 2D Exemplars},
author={Henzler, Philipp and Mitra, Niloy J and Ritschel, Tobias},
booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}
month={June},
year={2020}
}
```
##### Side Note
Unlike reported in the paper the encoder network in this implementation uses a ResNet architecture as it stabilises training.