Aesthetic-Aware Image Style Transfer
Zhiyuan Hu, Jia Jia, Bei Liu, Yaohua Bu, Jianlong Fu
摘要
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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引用它的顶会 Paper9
- Domain-Aware Universal Style TransferKibeom Hong, Seogkyu Jeon, Huan Yang, Jianlong Fu 等ICCV 2021 · 被引用 77 次
- AesUST: Towards Aesthetic-Enhanced Universal Style TransferZhizhong Wang, Zhanjie Zhang, Lei Zhao, Zhiwen Zuo 等ACM MM 2022 · 被引用 70 次
- MEmoR: A Dataset for Multimodal Emotion Reasoning in VideosGuangyao Shen, Xin Wang, Xuguang Duan, Hongzhi Li 等ACM MM 2020 · 被引用 38 次
- Improving Visual Quality of Image Synthesis by A Token-based Generator with TransformersYanhong Zeng, Huan Yang, Hongyang Chao, Jianbo Wang 等NeurIPS 2021 · 被引用 31 次
- TSSAT: Two-Stage Statistics-Aware Transformation for Artistic Style TransferHaibo Chen, Lei Zhao, Jun Li, Jian YangACM MM 2023 · 被引用 21 次
它引用的顶会 Paper3
- Photorealistic Style Transfer via Wavelet TransformsJaejun Yoo, Youngjung Uh, Sanghyuk Chun, Byeongkyu Kang 等ICCV 2019 · 被引用 412 次
- A Closed-Form Solution to Universal Style TransferMing Lu, Hao Zhao, Anbang Yao, Yurong Chen 等ICCV 2019 · 被引用 91 次
- MEmoR: A Dataset for Multimodal Emotion Reasoning in VideosGuangyao Shen, Xin Wang, Xuguang Duan, Hongzhi Li 等ACM MM 2020 · 被引用 38 次
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