An Automatic Texture Generation Algorithm for 3D Shapes Based on cGAN

Abstract

Texturing 3D shapes is of great importance in computer graphics with applications ranging from game design to augmented reality. However, the processes of texture generation are usually tedious, time-consuming and labor-intensive. In this paper, we propose an automatic texture generation algorithm for 3D shapes based on conditional Generative Adversarial Networks (cGAN). The core of our algorithm includes sampling the model outline and building a cGAN in order to generate model textures automatically. In particular, we propose a novel edge detection method using 3D model information which can accurately find the outline of the model to improve the quality of the generated texture. Due to the adaptability of the algorithm, our approach is suitable for texture generation for most 3D models. Experimental results show the efficiency of our algorithm which can easily generate high quality model textures.