StyTr^2: Unbiased Image Style Transfer with Transformers

• URL: http://arxiv.org/abs/2105.14576v1
• Date: Sun, 30 May 2021 15:57:09 GMT
• Title: StyTr^2: Unbiased Image Style Transfer with Transformers
• Authors: Yingying Deng and Fan Tang and Xingjia Pan, Weiming Dong and ChongyangMa and Changsheng Xu
• Abstract summary: The goal of image style transfer is to render an image with artistic features guided by a style reference while maintaining the original content. Traditional neural style transfer methods are usually biased and content leak can be observed by running several times of the style transfer process with the same reference image. We propose a transformer-based approach, namely StyTr2, to address this critical issue.
• Score: 59.34108877969477
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The goal of image style transfer is to render an image with artistic features guided by a style reference while maintaining the original content. Due to the locality and spatial invariance in CNNs, it is difficult to extract and maintain the global information of input images. Therefore, traditional neural style transfer methods are usually biased and content leak can be observed by running several times of the style transfer process with the same reference style image. To address this critical issue, we take long-range dependencies of input images into account for unbiased style transfer by proposing a transformer-based approach, namely StyTr^2. In contrast with visual transformers for other vision tasks, our StyTr^2 contains two different transformer encoders to generate domain-specific sequences for content and style, respectively. Following the encoders, a multi-layer transformer decoder is adopted to stylize the content sequence according to the style sequence. In addition, we analyze the deficiency of existing positional encoding methods and propose the content-aware positional encoding (CAPE) which is scale-invariant and more suitable for image style transfer task. Qualitative and quantitative experiments demonstrate the effectiveness of the proposed StyTr^2 compared to state-of-the-art CNN-based and flow-based approaches.

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