Official code repository for the paper:
Learning a Neural 3D Texture Space from 2D Exemplars [CVPR, 2020]
[Paper] [Project page]
We downloaded all our textures from 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.
In order to evaluate textures, add the desired texture to the corresponding folder under datasets/<class_name>/test and use one of the pre-trained models under trained_models/ and run the evaluation (see instructions below). We already provide some exemplars.
For training you will need to provide data sets under datasets/<your_folder> 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.
- Ubuntu 18.04
- cuDNN 7
- CUDA 10.1
- python3+
- pyTorch 1.4
- Download pretrained models (optional)
cd code/
pip install -r requirements.txt
cd custom_ops/noise
# build cuda code for noise sampler
TORCH_CUDA_ARCH_LIST=<desired version> python setup.py install
sh download_pretrained_models.sh
To visualise pre-trained training logs run the following:
tensorboard --logdir=./trained_models
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
cd code/
python train_neural_texture.py --config_path=<path/to/config> --job_id=<your_id>
The default config_path is set to configs/neural_texture/config_default.yaml. The default job_id is set to 1.
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
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}
}
Unlike reported in the paper the encoder network in this implementation uses a ResNet architecture as it stabilises training.
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