  This paper proposes an image-to-painting translation method that generates
vivid and realistic painting artworks with controllable styles. Different from
previous image-to-image translation methods that formulate the translation as
pixel-wise prediction, we deal with such an artistic creation process in a
vectorized environment and produce a sequence of physically meaningful stroke
parameters that can be further used for rendering. Since a typical vector
render is not differentiable, we design a novel neural renderer which imitates
the behavior of the vector renderer and then frame the stroke prediction as a
parameter searching process that maximizes the similarity between the input and
the rendering output. We explored the zero-gradient problem on parameter
searching and propose to solve this problem from an optimal transportation
perspective. We also show that previous neural renderers have a parameter
coupling problem and we re-design the rendering network with a rasterization
network and a shading network that better handles the disentanglement of shape
and color. Experiments show that the paintings generated by our method have a
high degree of fidelity in both global appearance and local textures. Our
method can be also jointly optimized with neural style transfer that further
transfers visual style from other images. Our code and animated results are
available at \url{https://jiupinjia.github.io/neuralpainter/}.
