Visual style transfer involves combining the content of one image with the style of another, and recent work has produced some compelling results. This paper proposes a related task that requires additional system intelligence and autonomy—that of style creation. Rather than using the style of an existing source image, the goal is to have the system autonomously create a rendering style based on a simple (text- based) semantic description. Results demonstrate the system’s ability to autonomously create interesting, semantically appropriate styles that can be applied for image rendering.

When dealing with images and semantics, most computational systems attempt to automatically extract meaning from images. Here we attempt to go the other direction and autonomously create images that communicate concepts. We present an enhanced semantic model that is used to generate novel images that convey meaning. We employ a vector space model and a large corpus to learn vector representations of words and then train the semantic model to predict word vectors that could describe a given image. Once trained, the model autonomously guides the process of rendering images that convey particular concepts. A significant contribution is that, because of the semantic associations encoded in these word vectors, we can also render images that convey concepts on which the model was not explicitly trained. We evaluate the semantic model with an image clustering technique and demonstrate that the model is successful in creating images that communicate semantic relationships.