Artistic Arbitrary Style Transfer
This work addresses the problem of applying artistic styles consistently in image generation for users in computer vision and graphics, but it is incremental as it builds on existing techniques like SANet and Detectron 2.
The paper tackles the challenge of balancing structure and style in arbitrary style transfer by using a deep learning approach with Convolutional Neural Networks, resulting in a method that extracts foreground from background and stitches styled images to maintain consistency.
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.