Research on Animation Character Based on the Semantic Segmentation Model

Abstract

A semantic ontology-driven hierarchical consistency segmentation algorithm was proposed to solve the segmentation inconsistency of animation character models because of the changing of poses. The mapping between semantic labels and local geometric features was extracted to form a segmentation ontology. In the process of segmentation, support vector machine (SVM) and local geometric features were used to identify the semantic labels, and segmentation was carried out according to the semantic label driving hierarchy to ensure the consistency of the segmentation levels of animation character models. Poisson equation was used to define contour lines for the equal perimeters of segmentation boundary because of the changing of poses. This optimization method made the segmentation boundary smooth and consistent under the changing of poses. In the experiment part, all kinds of animation character models under different poses were verified and analyzed, and the consistent hierarchical segmentation effect was obtained. Compared with the existing methods, the proposed segmentation ontology can solve the problem of adaptive selection of optimization parameters of different classes of models, and improve the segmentation quality. With the continuous development of deep learning, the use of image segmentation for animation, and human pose recognition will become more and more important.