Automatic Content-Aware Color and Tone Stylization

Understand

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.