Interactive texture replacement of cartoon characters based on deep learning model

Abstract

Abstract To understand the deep learning model, the author proposed the research of interactive texture replacement of cartoon characters. For image segmentation, if you want to fill a cartoon without any texture in detail, or replace the unsatisfied texture area, first, we need to separate the filled or replaced area from the cartoon. For this reason, the traditional image segmentation algorithm has been carefully studied and analyzed, and the author chooses the Graphcut texture synthesis algorithm, the algorithm is parallelized and improved, and the innovative point of lighting customization is proposed based on the original algorithm, which can affect the synthesis effect according to the input lighting image. In terms of timeliness and synthesis effect, the Graphcut algorithm has been improved. Experimental results show that the algorithm can maintain the brightness distribution of the original cartoon and the practicability and efficiency of the algorithm proposed by the author.