Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization

Parametric generative deep models are state-of-the-art for photo and non-photo realistic image stylization. However, learning complicated image representations requires computeintense modelsparametrized by a huge number of weights, which in turn requires large datasets to make learning successful. Nonparametric exemplar-based generation is a technique that works well to reproduce style from small datasets, but is also computeintensive. Theseaspects are a drawback for the practice of digital AI artists: typically one wants to use a small set of stylization images, and needs a fast flexible model in order to experiment with it. With this motivation, our work has these contributions: (i) a novel stylization method called Fully Adversarial Mosaics (FAMOS) that combines the strengths of both parametric and nonparametric approaches; (ii) multiple ablations and image examples that analyze the method and show its capabilities; (iii) source code that will empower artists and machine learning researchers to use and modify FAMOS. Tiling of small stones was a classical ancient art form, and in modern times there are efficient algorithms to produce such mosaics (with non-overlapping tiles) digitally [10]. Seamless mosaics in the style of the Renaissance painter Archimboldo are more challenging, but modern deep learning methods allow efficient seamless image stylization. Neural style transfer [4] uses filter statistics (pretrained on a huge dataset) of a style image to optimize an output image.