Skip to content

Latest commit

 

History

History
256 lines (205 loc) · 11.1 KB

README.md

File metadata and controls

256 lines (205 loc) · 11.1 KB
Raw

arXiv Open with Colab

Based on the improvements of Katherine Crowson (Neural style transfer in PyTorch) on the paper A Neural Algorithm of Artistic Style, This repository brings some changes to enhance the style transfer results on images that contains human faces.

How it Works?

Figure 1: Stylization Diagram.

First we pass the original image to the MODNet Human Segmentation Model to generate an alpha layer and remove the background. Then the image is forwarded through the FaceNet, FaceMesh and VGG19 Models for feature extraction. The VGG19 Model is also used to extract the features from desired style images. And finally, all the extracted features are joined to create the Content, Facial and Style losses.

Facial Identification Loss

Using FaceNet Inception Resnet Model (Tim Esler), with VGGFace2 Pretrained weights, We can implement a FaceID Loss, comparing the internal representations of the model from content image and result image with MSE (Mean Squared Error), like in the VGG Model Content Loss.

The FaceID Loss weight can be controlled through face_weight argument.

Facial Meshes Loss

The FaceMesh Loss works like the FaceID Loss, but it uses only the last layer output from FaceMesh Model (George Grigorev) that represents the Facial 3D Meshes. Use only for fine adjustments on relevant expression attributes like mouth opening.

The FaceMesh Loss weight can be controlled through face_mesh argument.

MODNet Background Removal

The BackgroundRemoval class uses MODNet: Trimap-Free Portrait Matting in Real Time as backend, and uses human matting pretrained weights provided by the author. You can remove the background from input image for better results, but it's not necessary.

The MODNet Model can be used through the remove_bg.py script and the BackgroundRemoval class.

Experiments

The content_weight parameter focus on general image content, while the face_weight parameter focus on specific facial attributes. mesh_weight is helpful when adjusting finer details on result faces.

Note: crop_faces parameter is set to False in all the experiments below.

  • content_weight: 0.05
  • face_weight: 0.25
  • mesh_weight: 0.015
  • content_weight: 0.0
  • face_weight: 0.25
  • mesh_weight: 0.015
  • content_weight: 0.05
  • face_weight: 0.0
  • mesh_weight: 0.015
  • content_weight: 0.05
  • face_weight: 0.25
  • mesh_weight: 0.0

Installation

Here you can find instructions to install the project through conda. We'll create a new environment, and install the required dependencies.

First, clone the repository locally:

git clone https://github.com/thiagoambiel/PortraitStylization.git
cd PortraitStylization

Create a new conda environment called portrait_stylization:

conda create -n portrait_stylization python=3.7
conda activate portrait_stylization

Now install the project dependencies:

conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
pip install -r requirements.txt

Basic Usage

You can use the CLI tools remove_bg.py and stylize.py or import the classes BackgroundRemoval and StyleTransfer. Both methods will download the VGG19 Weights (548 MB) and FaceNet VGGFace2 Weights (107 MB) at the first run.

Input images will be converted to sRGB when loaded, and output images have the sRGB color space. Alpha channels in the inputs will be ignored.

Load as Module

The StyleTransfer and BackgroundRemoval classes can be used on an interactive python session or in a common script.

from PIL import Image
from style_transfer import StyleTransfer
from remove_bg import BackgroundRemoval

# Load content image.
original_image = Image.open("content.jpg")

# Load MODNet and remove content image background.
background_removal = BackgroundRemoval("./weights/modnet.pth", device="cpu")

content_image = background_removal.remove_background(
    img=original_image,
    bg_color="black",
)

# Load style images.
style_images = [
    Image.open("style_1.jpg"), 
    Image.open("style_2.jpg"),
]

# Load and run style transfer module.
st = StyleTransfer(device="cpu", pooling="max")

result_image = st.stylize(
    content_image=content_image, 
    style_images=style_images,
    content_weight=0.05,
    face_weight=0.25,
)

# Save result to disk.
result_image.save("out.png")

Run from Command Line

  • Background Removal

python remove_bg.py content.jpg output.jpg

  • content.jpg: The input image to remove the background.

  • output.jpg: The path to save the result image.

You can run python remove_bg.py --help for more info.

  • Style Transfer

python stylize.py input.jpg style_1.jpg style_2.jpg -o out.png -cw 0.05 -fw 0.25

  • input.jpg: The input image to be stylized.

  • style_N.jpg: The style images that will be used to stylize the content image, need to be at least one.

  • -o out.png (--save-path): The path to save the result image.

  • -cw 0.05 (--content-weight): The Content Loss weight. It defines how similar the content of result image will be when compared with the content image.

  • -fw 0.25 (--face-weight): The FaceID Loss weight. It defines how similar the detected faces in result image will be when compared with the content image.

You can run python stylize.py --help for more info.

Example Results

Note: It also works with multiple faces in the same image.

Content Style Result

Acknowledgements

Thanks to the authors of these amazing projects.

License

PortraitStylization is released under the MIT license. Please see the LICENSE file for more information.