  Arbitrary Style Transfer is a technique used to produce a new image from two
images: a content image, and a style image. The newly produced image is unseen
and is generated from the algorithm itself. Balancing the structure and style
components has been the major challenge that other state-of-the-art algorithms
have tried to solve. Despite all the efforts, it's still a major challenge to
apply the artistic style that was originally created on top of the structure of
the content image while maintaining consistency. In this work, we solved these
problems by using a Deep Learning approach using Convolutional Neural Networks.
Our implementation will first extract foreground from the background using the
pre-trained Detectron 2 model from the content image, and then apply the
Arbitrary Style Transfer technique that is used in SANet. Once we have the two
styled images, we will stitch the two chunks of images after the process of
style transfer for the complete end piece.
