Line Artist: A Multiple Style Sketch to Painting Synthesis Scheme
This addresses the challenge of creating beautiful paintings from sketches for artists or hobbyists, but it appears incremental as it builds on existing deep learning and style transfer methods.
The paper tackles the problem of synthesizing artistic paintings from freehand sketches by proposing Line Artist, a scheme that combines sketch extraction, detailed image synthesis, and adaptive style transfer, resulting in artistic and robust images as validated on datasets like Kaggle Cats and Oxford Buildings.
Drawing a beautiful painting is a dream of many people since childhood. In this paper, we propose a novel scheme, Line Artist, to synthesize artistic style paintings with freehand sketch images, leveraging the power of deep learning and advanced algorithms. Our scheme includes three models. The Sketch Image Extraction (SIE) model is applied to generate the training data. It includes smoothing reality images and pencil sketch extraction. The Detailed Image Synthesis (DIS) model trains a conditional generative adversarial network to generate detailed real-world information. The Adaptively Weighted Artistic Style Transfer (AWAST) model is capable to combine multiple style images with a content with the VGG19 network and PageRank algorithm. The appealing artistic images are then generated by optimization iterations. Experiments are operated on the Kaggle Cats dataset and The Oxford Buildings Dataset. Our synthesis results are proved to be artistic, beautiful and robust.