Artificial intelligence (AI) has played a significant role in imitating and producing large-scale designs such as e-commerce 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 co-creative 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 human’s sketch. Experiments confirmed that the proposed SmartPaint system successfully generates high-quality cartoon paintings.
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Compliance with ethics guidelines
Lingyun SUN, Pei CHEN, Wei XIANG, Peng CHEN, Wei-yue GAO, and Ke-jun ZHANG declare that they have no conflict of interest.
Project supported by the National Science and Technology Innovation 2030 Major Project of the Ministry of Science and Technology of China (No. 2018AAA0100703), the National Natural Science Foundation of China (No. 61672451), the Provincial Key Research and Development Plan of Zhejiang Province, China (No. 2019C03137), the China Postdoctoral Science Foundation (No. 2018M630658), and the Ng Teng Fong Charitable Foundation in the form of ZJU-SUTD IDEA Grant
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Sun, L., Chen, P., Xiang, W. et al. SmartPaint: a co-creative drawing system based on generative adversarial networks. Front Inform Technol Electron Eng 20, 1644–1656 (2019). https://doi.org/10.1631/FITEE.1900386
- Co-creative drawing
- Deep learning
- Image generation