Automatic portrait oil painter: joint domain stylization for portrait images

Everyone has the dream of being in the center of famous art paintings, admired by numerous future generations. However, the dream came true at a huge cost of the painter’s commission in old days. In our paper, another practical choice is provided for everyone to achieve that dream – an automatic portrait oil painter transferring some artistic styles from one single reference painting. To address this issue, we propose a joint-domain image stylization approach, particularly for portrait oil paintings. From the view of artistic appreciation, we analyze an amount of oil painting art works and summarize three critical factors to depict the figure, i.e. color, structure and texture. Based on this point, we separate and represent an artistic work into these three domains. Then, considering their intrinsic properties and following an art creation route, we propose the corresponding approaches to jointly model and transfer the features in these domains. First, a swatch-based color adjustment is proposed to recolor the tone of the input image based on semantic regions corresponding to the references. Second, the main structures of the input image is maintained by sparse reconstruction. Third, a coarse-to-fine texture synthesis is used to enhance the detail oil painting patterns. Extensive experimental results demonstrate that the proposed method achieves desirable results compared with state-of-the-art methods in not only transferring the styles from references but also keeping consistent contents with the given portrait.