Semantics-driven portrait cartoon stylization
This paper proposes an efficient framework for transforming an input human portrait image into an artistic cartoon style. Compared to the previous work of non-photorealistic rendering (NPR), our method exploits the portrait semantics for enriching and manipulating the cartooning style, based on a semantic grammar model. The proposed framework consists of two phases: a portrait parsing phase to localize and recognize facial components in a hierarchic manner, and further calculate the portrait saliency with the facial components; a cartoon stylizing phase to abstract and cartoonize the portrait according to the parsed semantics and saliency, in which the regions and structure (edges/boundaries) of the portrait are rendered in two layers. In the experiments, we test our method with different types of human portraits: daily photos, identification photos, and studio photos, and find satisfactory results; a quantitative evaluation of subjective preference is presented as well.