Transformation of Semantic Painting Image Style by Using Convolutional Neural Networks

Abstract

Photographic portrait style is most significant in these days, most scholars are trying to yield a noble effort to modify the portrait style, however, seldom of them concentrate on painting style. The painting style is interesting and by means of a human graphic method, it is delicate and show the little bit indiscretions in creating human faces and physique configurations. The widely style relocation is trying to include semantic material into the old-style method. This exercise attains superior perseverance consequences by moving the styles between semantically consistent areas. In this article, we discuss an advanced framework for good and advanced semantic painting style transfer and introduce a new technique, the semantic painting style transfer to solve the display, characteristic rebuilding and the characteristic photographic problems. By cautiously linking the original look to the painted version, the future methodology not gain reasonable consequences as progressive optimization systems, but also helps to figure out which method is greater and quicker. We believe this study clarification attaches these two significant investigation methods and clarify forthcoming researches. However, this study future method is not limited to shots pictures or exact styles as this study technique considers the alterations of the semantic painting style.