Generating New Paintings by Semantic Guidance

In order to facilitate the human painting process, numerous research efforts have been made on teaching machines how to “paint like a human”, which is a challenging problem. Recent stroke-based rendering algorithms generate non-photorealistic imagery using a number of strokes to mimic a target image. However, the applicability of previous methods can only draw the content of one target image on a canvas that limits generation ability. We propose a novel painting approach which teach machines to paint with multiple target images and then generate new paintings. We consider the order of human painting and propose a combined stroke rendering method that can merge the content of multiple images into the same painting. We use semantic segmentation to obtain semantic information in multiple images, and add the semantic information in different images to the same painting process. Finally, our model can generate new paintings with contents from different images with the guidance of this semantic information. Experimental results demonstrate that our model can effectively generate new paintings which can assist human beings to create.