Aesthetic-Aware Image Style Transfer
Zhiyuan Hu, Jia Jia, Bei Liu, Yaohua Bu, Jianlong Fu
Abstract
Style transfer aims to synthesize an image which inherits the content of one image while preserving a similar style of the other one. The "style'' of an image usually refers to its unique feeling conveyed from visual features, which is highly related to the aesthetic effect of the image. Aesthetic effect can be mainly decomposed as two factors: colour and texture. Previous methods like Neural Style Transfer and Colour Transfer have shown strong abilities in transferring colour and texture features. However, such approaches neglect to further disentangle colour and texture, which makes some of unique aesthetic effects designed by human artists hard to express. In this paper, we propose a novel problem called Aesthetic-Aware Image Style Transfer task, which aims to transfer colour and texture separately and independently to manipulate the aesthetic effect of an image. We propose a novel Aesthetic-Aware Model-Optimisation-Based Style Transfer (AAMOBST) model to solve this problem. Specifically, AAMOBST is a multi-reference, two-path model. It uses different reference images to decide desired colour and texture features. It can segregate colour and texture into two distinct paths and transfer them independently. Qualitative and quantitative experiments show that our model can decide colour and texture features separately and is able to keep one of them fixed while changing the other one, which is not applicable for previous methods. Furthermore, on tasks that are applicable for previous methods (such as style transfer, colour-preserved transfer and colour-only transfer), our model shows comparable abilities with other baseline methods.
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Install the CLIlune papers fulltext c668bc71-b576-4d56-97e2-f6f303ce30ebCited by top-tier papers9
- Domain-Aware Universal Style TransferKibeom Hong, Seogkyu Jeon, Huan Yang, Jianlong Fu et al.ICCV 2021 · 77 citations
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- Improving Visual Quality of Image Synthesis by A Token-based Generator with TransformersYanhong Zeng, Huan Yang, Hongyang Chao, Jianbo Wang et al.NeurIPS 2021 · 31 citations
- TSSAT: Two-Stage Statistics-Aware Transformation for Artistic Style TransferHaibo Chen, Lei Zhao, Jun Li, Jian YangACM MM 2023 · 21 citations
Builds on3
- Photorealistic Style Transfer via Wavelet TransformsJaejun Yoo, Youngjung Uh, Sanghyuk Chun, Byeongkyu Kang et al.ICCV 2019 · 412 citations
- A Closed-Form Solution to Universal Style TransferMing Lu, Hao Zhao, Anbang Yao, Yurong Chen et al.ICCV 2019 · 91 citations
- MEmoR: A Dataset for Multimodal Emotion Reasoning in VideosGuangyao Shen, Xin Wang, Xuguang Duan, Hongzhi Li et al.ACM MM 2020 · 38 citations
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