Conditional Generative Adversarial Network 기반 카툰원화의 Line drawing 추출

Abstract

Recently, 3D contents used in various fields have been attracting people's attention due to the development of virtual reality and augmented reality technology. In order to produce 3D contents, it is necessary to model the objects as vertices. However, high-quality modeling is time-consuming and costly. In this work, we propose a method, based onconditional adversarial network, for automatic extraction of line drawings that show the geometrical characteristics of 3D models in 2D cartoon painting. Drawing-based modeling among non-photorealistic expressions is a technique that shortens the time required for modeling. It refers to creating a 3D model based on a user's line drawing or sketch. Line drawings are used to represent the shape of objects using a line that is a minimum of data. Line drawings require feature line extraction to determine which part of the object should be represented as a line. Extracting feature lines provides more information about a 3D model. In order to convert a 2D character into a 3D model, it is necessary to express it as line drawings through feature line extraction. The extraction of consistent line drawings from 2D cartoon cartoons is difficult because the styles and techniques differ depending on the designer who produces them. Therefore, it is necessary to extract the line drawings that show the geometrical characteristics well in 2D cartoon shapes of various styles. This study proposes a method of automatically extracting line drawings. The 2D Cartoon shading image and line drawings are learned by using conditional adversarial network model, which is artificial intelligence technology and outputs 2D cartoon artwork of various styles.Experimental results show the proposed method in this research can be obtained as a result of the line drawings representing the geometric characteristics when a 2D cartoon painting as input.