@inproceedings{
yi2024aesstyler,
title={AesStyler: Aesthetic Guided Universal Style Transfer},
author={Ran Yi and Haokun Zhu and Teng Hu and Yu-Kun Lai and Paul L Rosin},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=IiDczMpPFn}
}
Recent studies have shown impressive progress in universal style transfer which can integrate arbitrary styles into content images. However, existing approaches struggle with low aesthetics and disharmonious patterns in the final results. To address this problem, we propose AesStyler, a novel Aesthetic Guided Universal Style Transfer method. Specifically, our approach introduces the aesthetic assessment model, trained on a dataset with human-assessed aesthetic scores, into the universal style transfer task to accurately capture aesthetic features that universally resonate with human aesthetic preferences. Unlike previous methods which only consider aesthetics of specific style images, we propose to build a Universal Aesthetic Codebook (UAC) to harness universal aesthetic features that encapsulate the global aspects of aesthetics. Aesthetic features are fed into a novel Universal and Style-specific Aesthetic-Guided Attention (USAesA) module to guide the style transfer process. USAesA empowers our model to integrate the aesthetic attributes of both universal and style-specific aesthetic features with style features and facilitates the fusion of these aesthetically enhanced style features with content features. Extensive experiments and user studies have demonstrated that our approach generates aesthetically more harmonious and pleasing results than the state-of-the-art methods, both aesthetic-free and aesthetic-aware.
AesStyler: Aesthetic Guided Universal Style Transfer
Ran Yi Haokun Zhu Teng Hu Yu-Kun Lai Paul L Rosin