StrokeNet: A neural painting environment. In: Proceedings of the International Conference on Learning Representations, 1–16, 2018. A neural representation of sketch drawings. A multi-level depiction method for painterly rendering based on visual perception cue. In: Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques, 453–460, 1998. Painterly rendering with curved brush strokes of multiple sizes. Paint by numbers: Abstract image representations. Jittor-GAN: A fast-training generative adversarial network model zoo based on Jittor. In: Proceedings of the 25th ACM International Conference on Multimedia, 244–251, 2017. Stylized adversarial AutoEncoder for image generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2414–2423, 2016. Image style transfer using convolutional neural networks. Experiments demonstrate that our artful painter can generate various styles of paintings while well-preserving content details (such as details of human faces and building textures) and retaining high fidelity to the input images. To overcome the third limitation, we have devised a neural network named Stroke-GAN Painter, based on Stroke-GAN it can generate different artistic styles of paintings. We design three players in Stroke-GAN to generate pure-color strokes close to human artists’ strokes, thereby improving the quality of painted details. Stroke-GAN learns styles of strokes from different stroke-style datasets, so can produce diverse stroke styles. In this paper, we propose a stroke-style generative adversarial network, called Stroke-GAN, to solve the first two limitations. Despite advances in stroke-based image rendering and deep learning-based image rendering, existing painting methods have limitations: they (i) lack flexibility to choose different art-style strokes, (ii) lose content details of images, and (iii) generate few artistic styles for paintings. It is a challenging task to teach machines to paint like human artists in a stroke-by-stroke fashion.
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