# Style Transfer With Adaptation to the Central Objects of the Scene

## TL;DR

This paper introduces a style transfer method that detects central objects in images and applies style non-uniformly to preserve the recognizability of key objects like faces or text, improving visual quality.

## Contribution

It proposes a novel style transfer algorithm with automatic central object detection and spatial importance masking, enhancing stylization quality over classical methods.

## Key findings

- Higher quality stylization compared to classical methods
- Three automatic central object detection methods evaluated
- User study confirms improved preservation of key objects

## Abstract

Style transfer is a problem of rendering image with some content in the style of another image, for example a family photo in the style of a painting of some famous artist. The drawback of classical style transfer algorithm is that it imposes style uniformly on all parts of the content image, which perturbs central objects on the content image, such as faces or text, and makes them unrecognizable. This work proposes a novel style transfer algorithm which automatically detects central objects on the content image, generates spatial importance mask and imposes style non-uniformly: central objects are stylized less to preserve their recognizability and other parts of the image are stylized as usual to preserve the style. Three methods of automatic central object detection are proposed and evaluated qualitatively and via a user evaluation study. Both comparisons demonstrate higher quality of stylization compared to the classical style transfer method.