musetee / cap-vstnet

[CVPR 2023] CAP-VSTNet: Content Affinity Preserved Versatile Style Transfer

CAP-VSTNet: Content Affinity Preserved Versatile Style Transfer (CVPR 2023)

Paper | Poster | Video 1 | Video 2

Usage

Three ways of using CAP-VSTNet to stylize image/video.

• Style transfer without using semantic masks.
• Style transfer with manually generated semantic masks.
• Style transfer with automatically generated semantic masks.

Requirements

1.It's compatible with pytorch>=1.0. An example (without using semantic segmentation model):

conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
pip install opencv-python scipy tqdm

2.If you want to transfer style with automatically generated semantic mask, an example using segmentation model SegFormer (test on Linux):

conda create --name capvst python=3.8 -y
conda activate capvst
pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
pip install mmcv-full==1.3.0 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/index.html
pip install timm opencv-python ipython attrs scipy

cd models/segmentation & git clone https://github.com/NVlabs/SegFormer.git
cd SegFormer && pip install -e . --user

Then, download the pre-trained weight segformer.b5.640x640.ade.160k.pth (google drive | onedrive) and save at models/segmentation/SegFormer/segformer.b5.640x640.ade.160k.pth.

Test

Download the pre-trained weight in checkpoints directory (google drive).

Image Style Transfer

CUDA_VISIBLE_DEVICES=0 python image_transfer.py --mode photorealistic --ckpoint checkpoints/photo_image.pt --content data/content/01.jpg  --style data/style/01.jpg

CUDA_VISIBLE_DEVICES=0 python image_transfer.py --mode artistic --ckpoint checkpoints/art_image.pt --content data/content/02.jpg  --style data/style/02.png

• mode: photorealistic or artistic.
• ckpoint: path to the model checkpoint.
• content: path to the content image.
• style: path to the style image.
• auto_seg: set True to use segmentation model (e.g. SegFormer).
• content_seg (optional): path to the manually generated content segmentation if auto_seg=False.
• style_seg (optional): path to the manually generated style segmentation if auto_seg=False.
• max_size: maximum output image size of long edge.
• alpha_c: interpolation between content and style if segmentation is None.

Note: Using the video model ckpoint also works. If content_seg and style_seg are provided, it's recommended to use lossless files (e.g., png) as the compression files (e.g., jpg) may have noise label.

Video Style Transfer

CUDA_VISIBLE_DEVICES=0 python video_transfer.py --mode photorealistic --ckpoint checkpoints/photo_video.pt --video data/content/03.avi  --style data/style/03.jpeg

CUDA_VISIBLE_DEVICES=0 python video_transfer.py --mode artistic --ckpoint checkpoints/art_video.pt --video data/content/04.avi  --style data/style/04.jpg

• mode: photorealistic or artistic.
• ckpoint: path to the model checkpoint.
• video: path to the input video or frame directory.
• style: path to the style image.
• auto_seg: set True to use segmentation model (e.g. SegFormer).
• max_size: maximum output video size of long edge.
• alpha_c: interpolation between content and style if segmentation is None.
• fps: video frames per second

Note: Set --auto_seg True to automatically generate semantic segmentation for better stylization effects. For more information on how to automatically or manually generate semantic segmentation, please refer to Link (where we get inspiration and benefit a lot from).

Train

Download the pre-trained VGG19 (google drive) and save at checkpoints/vgg_normalised.pth.

Download dataset MS_COCO, WikiArt or your own dataset. You need to provide two folders for params --train_content and --train_style respectively. And the two folders can be the same.

An example of how a folder can look like.

/path_to_dir/img_1.jpg
/path_to_dir/img_2.png
/path_to_dir/img_3.png
...
/path_to_dir/sub_dirA/img_4.jpg
/path_to_dir/sub_dirB/sub_dirC/img_5.png
...

• Train photorealistic model

CUDA_VISIBLE_DEVICES=0 python train.py --mode photorealistic --train_content /path/to/COCO/directory  --train_style /path/to/COCO/directory

• Train artistic model

CUDA_VISIBLE_DEVICES=0 python train.py --mode artistic --train_content /path/to/COCO/directory  --train_style /path/to/WikiArt/directory --lap_weight 1 --rec_weight 1

Check log images at logs/XXX/index.html. After training, you will have the checkpoints of image model and video model in logs/XXX/checkpoints directory.

Results

Video Style Transfer

• Photorealistic video stylization and temporal error heatmap

• Artistic video stylization and temporal error heatmap

Style Interpolation

• Photorealistic style interpolation

• Artistic style interpolation

Ultra-resolution

An example of 4K images stylization

Flow, Temporal Loss and Heatmap

See issues#11

Citation

@inproceedings{wen2023cap,
  title={CAP-VSTNet: Content Affinity Preserved Versatile Style Transfer},
  author={Wen, Linfeng and Gao, Chengying and Zou, Changqing},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={18300--18309},
  year={2023}
}

Acknowledgement

We thank the great work PhotoWCT, LinearWCT and ArtFlow.