Learning to Manipulate Artistic Images
This work addresses the challenge of cross-domain artifacts and imprecise structure in artistic image manipulation for creators and researchers, though it appears incremental as it builds on existing exemplar-based translation methods.
The paper tackles the problem of generating artistic images without requiring accurate semantic information, which is difficult to obtain, by proposing SIM-Net, a method that uses semantic-free guidance and region transportation in a self-supervised manner, achieving superior performance over state-of-the-art methods in both qualitative and quantitative experiments.
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.