Compositional Model-Based Sketch Generator in Facial Entertainment
提出一种基于组合模型的人脸草图生成方法，将人脸分解为不同组件并选用模板，通过多级B样条逼近和泊松融合减少高频损失，提升面部娱乐中的草图合成效果。
Face sketch synthesis (FSS) plays an important role in facial entertainment, which includes face sketch morphing among two styles, multiview FSS and face sketch expression manipulation. For facial entertainment, most existing FSS methods generate sketches with over-smoothing effects, i.e., fine details are suppressed more or less. In this paper, we propose a face sketch generator based on the compositional model to handle this issue. It decomposes a face into different components instead of patches as before, and each component has several candidate templates. Multilevel B-spline approximation is utilized to delicately polish the chosen templates of all components. To fuse these components, Poisson blending is employed instead of the weighted average operator. The proposed compositional method crucially reduces the high frequency loss and improves the synthesis performance in comparison to the state-of-the-art methods. Experiments on face sketch morphing, expression manipulation, and multiview FSS, make further efforts to demonstrate the effectiveness of the proposed method.