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

arXiv: https://arxiv.org/abs/1908.11525 · HTML: https://mlsanity.com/paper/1908.11525/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 7 of 7)

- [StyleSplat: 3D Object Style Transfer with Gaussian Splatting](https://mlsanity.com/paper/2407.09473/) (2024)
- [Evaluation in Neural Style Transfer: A Review](https://mlsanity.com/paper/2401.17109/) (2024)
- [LISA: Localized Image Stylization with Audio via Implicit Neural Representation](https://mlsanity.com/paper/2211.11381/) (2022)
- [MOSAIC: Multi-Object Segmented Arbitrary Stylization Using CLIP](https://mlsanity.com/paper/2309.13716/) (2023)
- [Interactive Style Transfer: All is Your Palette](https://mlsanity.com/paper/2203.13470/) (2022)
- [Improved Object-Based Style Transfer with Single Deep Network](https://mlsanity.com/paper/2404.09461/) (2024)
- [Improving Masked Style Transfer using Blended Partial Convolution](https://mlsanity.com/paper/2508.05769/) (2025)

## Abstract (arXiv)

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