This is the code for the paper
Learning to Warp for Style Transfer
Project Page | Paper | Poster | Video
Our method performs non-parametric warping to match artistic geometric style. The above shows content, style (geometry+texture), and output images for a Picasso style transfer (left) and a Salvaor Dali style transfer (right).
https://user-images.githubusercontent.com/58788896/124906358-e30dd380-dfde-11eb-99b2-b7b640a6a8d9.mov
If you find this code useful for your research, please cite
```
@InProceedings{Liu21LWST,
author={Xiao-Chang Liu and Yong-Liang Yang and Peter Hall},
title={Learning to Warp for Style Transfer},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2021}
}
```
## Preresquisites
Dependencies:
* Geometric Warping: [VLFeat](http://www.vlfeat.org/) and [MatConvNet](http://www.vlfeat.org/matconvnet/)
* Texture Rendering: [PyTorch](http://pytorch.org/), [CUDA](https://developer.nvidia.com/cuda-downloads) and [cuDNN](https://developer.nvidia.com/cudnn)
Pre-trained Models:
* Download the [model](https://drive.google.com/uc?id=1PJJQ0KG2JYfJZDkU4ZOePndKJw63d7Yr&export=download) for geometric warping
```
cd geometric_warping
mkdir model
```
* Download the model for texture rendering
```
cd texture_rendering
python models/download_model.py
```
## Usage
### 1. Run geometric style transfer to warp the content image:
```
cd geometric_warping
run geo_warping.m [--STYLE_IMAGE] [--CONTENT_IMAGE]
```
After warping, empty background regions (if appear) are inpainted with pixels nearby.
### 2. Run texture style transfer to render the warped image:
```
cd texture_rendering
run multi_scale_st.sh [--STYLE_IMAGE] [--CONTENT_IMAGE] [--STYLE_WEIGHT]
```