Interactive Sketch-Based Normal Map Generation with Deep Neural Networks

Abstract

High-quality normal maps are important intermediates for representing complex shapes. In this paper, we propose an interactive system for generating normal maps with the help of deep learning techniques. Utilizing the Generative Adversarial Network (GAN) framework, our method produces high quality normal maps with sketch inputs. In addition, we further enhance the interactivity of our system by incorporating user-specified normals at selected points. Our method generates high quality normal maps in real time. Through comprehensive experiments, we show the effectiveness and robustness of our method. A thorough user study indicates the normal maps generated by our method achieve a lower perceptual difference from the ground truth compared to the alternative methods.