Semantic segmentation based automatic two-tone portrait synthesis

Abstract

This paper presents a semantic segmentation based method for automatically synthesizing two-tone cartoon portraits in black-and-white style. Synthesizing two-tone portraits from photographs can be considered as a heterogeneous image transformation problem, of which the result should be vivid portraits with distinct freehand-like features, such as clean backgrounds and continuous lines. To achieve this goal, our system connects two separate subsystems together, namely semantic segmentation and portrait synthesis. In the semantic segmentation phase, photographs are segmented into background, hair and skin regions using multiple segmentations method. In the portrait synthesis phase, we treat different regions with different strategies. Experimental results demonstrate that our system can precisely segment the input photo and produce visually desired two-tone portraits.