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.

GANILLA: Generative adversarial networks for image to illustration translation Abstract   In this paper, we explore illustrations in childrenu0027s books as a new domain in unpaired imagetoimage translation. We show that although the current stateoftheart imagetoimage translation models successfully transfer either the style or the content, they fail to transfer both at the same time. We propose a new generator network to address this issue and show that the resulting network strikes a better balance between style and content.  There are no welldefined or agreedupon evaluation metrics for unpaired imagetoimage translation. So far, the success of image translation models has been based on subjective, qualitative visual comparison on a limited number of images. To address this problem, we propose a new framework for the quantitative evaluation of imagetoillustration models, where both content and style are taken into account using separate classifiers. In this new evaluation framework, our proposed model performs better than the current stateoftheart models on the illustrations dataset. Our code and pretrained models can be found at  https:github.comgiddyyuppganilla .