A Drawing Software that Changes Users Realistic Experience by Sounds Generated by the Combination of Tools and Materials Today, many people have their own electronic devices, and use the internet. As a large part of the daily activities have now been digitized, many have no difficulty performing them by using the devices. Amid this digitization, a wide range of drawing software is evolving. From drawingpainting software that can be easily used by children and adults alike to those with specific features such as special software for product modeling and software with the texture of watercolor paints, different kinds of drawing software have been developed. Many creators and specialists use them which suit their purposes. While they all focus on providing functions and ease of use, few of the drawing software products represent realistic drawing experience. The sensation users have when working with analog tools creates emotional attachment to the tools. This leads to higher quality of the work and improved productivity. In addition, when drawing on paper with a pen or brush, unintended lines or points can be created. Though these cannot be easily corrected, they may add an intriguing touch to the work. With drawing software, this never happens, and making corrections is easy. In this research, we propose drawing software with realistic experience, which is one of the appeals of physical drawing as opposed to digitized drawing. In this paper, we focus on the realistic experience generated by sounds, and work on the sounds that vary by the combination of tools and materials.

SmartPaint: a cocreative drawing system based on generative adversarial networks Artificial intelligence (AI) has played a significant role in imitating and producing largescale designs such as ecommerce banners. However, it is less successful at creative and collaborative design outputs. Most humans express their ideas as rough sketches, and lack the professional skills to complete pleasing paintings. Existing AI approaches have failed to convert varied user sketches into artistically beautiful paintings while preserving their semantic concepts. To bridge this gap, we have developed SmartPaint, a cocreative drawing system based on generative adversarial networks (GANs), enabling a machine and a human being to collaborate in cartoon landscape painting. SmartPaint trains a GAN using triples of cartoon images, their corresponding semantic label maps, and edge detection maps. The machine can then simultaneously understand the cartoon style and semantics, along with the spatial relationships among the objects in the landscape images. The trained system receives a sketch as a semantic label map input, and automatically synthesizes its edge map for stable handling of varied sketches. It then outputs a creative and fine painting with the appropriate style corresponding to the humanxe2x80x99s sketch. Experiments confirmed that the proposed SmartPaint system successfully generates highquality cartoon paintings.