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We propose a Class-Based Styling method (CBS) that can map different stylesfor different object classes in real-time. CBS achieves real-time performanceby carrying out two steps simultaneously. While a semantic segmentation methodis used to obtain the mask of each object class in a video frame, a stylingmethod is used to style that frame globally. Then an object class can be styledby combining the segmentation mask and the styled image. The user can alsoselect multiple styles so that different object classes can have differentstyles in a single frame. For semantic segmentation, we leverage DABNet thatachieves high accuracy, yet only has 0.76 million parameters and runs at 104FPS. For the style transfer step, we use a popular real-time method proposed byJohnson et al. [7]. We evaluated CBS on a video of the CityScapes dataset andobserved high-quality localized style transfer results for different objectclasses and real-time performance.

Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation