Learning to Manipulate Artistic Images
Wei Guo Yuqi Zhang De Ma Qian Zheng
Recent advancement in computer vision has significantly lowered the barriers
to artistic creation. Exemplar-based image translation methods have attracted
much attention due to flexibility and controllability. However, these methods
hold assumptions regarding semantics or require semantic information as the
input, while accurate semantics is not easy to obtain in artistic images.
Besides, these methods suffer from cross-domain artifacts due to training data
prior and generate imprecise structure due to feature compression in the
spatial domain. In this paper, we propose an arbitrary Style Image Manipulation
Network (SIM-Net), which leverages semantic-free information as guidance and a
region transportation strategy in a self-supervised manner for image
generation. Our method balances computational efficiency and high resolution to
a certain extent. Moreover, our method facilitates zero-shot style image
manipulation. Both qualitative and quantitative experiments demonstrate the
superiority of our method over state-of-the-art methods.Code is available at
https://github.com/SnailForce/SIM-Net.