DrawNet: a neural painting network with attention module

We propose a image-to-painting translation method that can generate paintings on a stroke-by-stroke basis. Unlike previous pixel-to-pixel or sequential optimization methods, our method generates a set of physically meaningful stroke parameters, which are closer to the way humans draw. These parameters can be further rendered using a renderer. We add an attention mechanism network to the proposed renderer to improve the quality of the painting images and use smooth L1 loss in the training process of the renderer to make the model converge faster. Our method can join neural style transfer, and we used Visual Geometry Group perceptual loss in the neural style transfer stage to get more realistic results. The experimental results show that the renderer used in our method is better than other renderers, and peak signal-to-noise ratio evaluation metrics are improved by 4.9% compared with previous renderers.