Automatic Content-Aware Color and Tone Stylization
This addresses the need for high-quality, automated photo stylization without visual artifacts, though it appears incremental as it builds on existing style transfer methods.
The paper tackles the problem of automatically generating diverse and visually compelling stylizations for photographs in an unsupervised manner, achieving artifact-free results preferred in a user study.
We introduce a new technique that automatically generates diverse, visually compelling stylizations for a photograph in an unsupervised manner. We achieve this by learning style ranking for a given input using a large photo collection and selecting a diverse subset of matching styles for final style transfer. We also propose a novel technique that transfers the global color and tone of the chosen exemplars to the input photograph while avoiding the common visual artifacts produced by the existing style transfer methods. Together, our style selection and transfer techniques produce compelling, artifact-free results on a wide range of input photographs, and a user study shows that our results are preferred over other techniques.