https://dl.dropboxusercontent.com/u/91374104/web/paper/cvpr16_cast.pdfWe 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 an improved 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.
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|Joon-Young Lee, Kalyan Sunkavalli, Zhe Lin, Xiaohui Shen, In So Kweon
|IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [spotlight]