From Objects to a Whole Painting

Abstract

Style image painting is the process of using some stylized strokes to redraw a reference image purposefully and meaningfully. It is a kind of style image generation. In recent years, the application of GAN has greatly improved the quality of generated images for style image generation. However, those methods which use GAN are usually non-serialized. To solve this problem, reinforcement learning and RNN methods are applied to the generation of style images based on strokes. To speed up training, stroke-based style image painting using CNN framework is also proposed. But none of the existing style image painting methods takes into account the content distribution in the reference image, which makes the painting process lack a clear order. We extract the feature of the object contained in the image through the content acquisition module and use the optimal transmission theory to construct a compound loss function to integrate the content information into the painting process. As more advanced macro content information is added to the painting process, our painting method can draw images in a more orderly way. We have conducted a lot of experiments to prove that our method is superior to the current state-of-the-art style image painting method.