Baseline: On-the-fly stylization

On-the-fly stylization is a technique for applying artistic styles to images in real time. It involves using a pre-trained deep learning model to transfer the style of one image onto another image, creating a new image that combines the content of the original image with the style of the reference image.

The baseline approach for on-the-fly stylization involves using a pre-trained neural network, such as the VGG-19 network, to extract the content and style features from the input and reference images. These features are then used to compute the Gram matrix, which captures the correlations between the different feature maps and is used to represent the style of the reference image.

The content and style features are then combined to generate a new image that preserves the content of the input image while incorporating the style of the reference image. This is achieved by optimizing a loss function that balances the content and style losses.

The baseline approach has been extended in various ways, such as incorporating perceptual loss functions and using attention mechanisms to selectively transfer style to specific regions of the input image. Overall, on-the-fly stylization is a powerful technique for generating artistic images in real time and has applications in fields such as entertainment, advertising, and education.