포즈넷(PoseNet) 모형을 활용한 애니메이션 분석 연구: 애니메이션 캐릭터의 자세 추정을 중심으로

Abstract

The development of deep learning technology, a field of artificial intelligence, has opened up new possibilities for digital image research. In this study, the PoseNet, a posture estimation deep learning model, was outlined, and posture analysis of various animation characters was attempted using PoseNet. The motion data of animation characters such as 2D Cartoon Style, Anime Style, and 3D Style were classified into five stages based on the structural similarity of personification, and motion analysis and posture estimation were attempted through a PoseNet model. The similarity was measured by comparing the results of PoseNet posture estimation with the actual posture estimation of the character’s joint structure. As a result of the study, the closer the anatomical similarity and body ratio of animation characters approached the reality, the higher the performance of PoseNet and the lower the actual difference value in the analysis of pixel differences between PoseNet images and estimates. Through this, it was confirmed that deep learning models such as PoseNet can be used as an effective analysis tool for analyzing the motion of animation characters. In addition, the development of a model specialized in animation behavior and posture estimation of a specific style can be expected through the process of further sufficiently training animation data with large exaggerations.