Artistic image synthesis with tag-guided correlation matching

Artistic image synthesis is receiving increasing engagement in the multimedia community because of the development and improvement of generative adversarial networks. Digital art synthesis methods perform uncontrolled manipulation in complicated landscape scenarios because of the domain diversity of the paintings. To solve this problem, this paper presents the tag-guided correlation matrix for matching the tag with the content code from the source image as better guidance on the style code. Correspondingly, the new generator module adapts the warped style code onto the semantic feature for controllable synthesis. Extensive experiments in several image synthesis tasks show the effectiveness of the proposed method in generating images with variations or multi-tag combinations. In addition, the proposed method shows better quantitative performance than recent conditional image synthesis approaches in artistic image manipulation tasks.