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Distribution Aligned Multimodal and Multi-domain Image Stylization
ACM Transactions on Multimedia Computing, Communications, and Applications ( IF 5.1 ) Pub Date : 2021-07-22 , DOI: 10.1145/3450525 Minxuan Lin 1 , Fan Tang 2 , Weiming Dong 3 , Xiao Li 4 , Changsheng Xu 5 , Chongyang Ma 6
ACM Transactions on Multimedia Computing, Communications, and Applications ( IF 5.1 ) Pub Date : 2021-07-22 , DOI: 10.1145/3450525 Minxuan Lin 1 , Fan Tang 2 , Weiming Dong 3 , Xiao Li 4 , Changsheng Xu 5 , Chongyang Ma 6
Affiliation
Multimodal and multi-domain stylization are two important problems in the field of image style transfer. Currently, there are few methods that can perform multimodal and multi-domain stylization simultaneously. In this study, we propose a unified framework for multimodal and multi-domain style transfer with the support of both exemplar-based reference and randomly sampled guidance. The key component of our method is a novel style distribution alignment module that eliminates the explicit distribution gaps between various style domains and reduces the risk of mode collapse. The multimodal diversity is ensured by either guidance from multiple images or random style codes, while the multi-domain controllability is directly achieved by using a domain label. We validate our proposed framework on painting style transfer with various artistic styles and genres. Qualitative and quantitative comparisons with state-of-the-art methods demonstrate that our method can generate high-quality results of multi-domain styles and multimodal instances from reference style guidance or a random sampled style.
中文翻译:
分布对齐的多模态和多域图像样式化
多模态和多域风格化是图像风格迁移领域的两个重要问题。目前,很少有方法可以同时执行多模态和多域样式化。在这项研究中,我们提出了一个多模式和多领域风格迁移的统一框架,同时支持基于示例的参考和随机抽样的指导。我们方法的关键组成部分是一种新颖的样式分布对齐模块,它消除了各种样式域之间的显式分布差距并降低了模式崩溃的风险。多模态多样性通过来自多个图像的引导或随机样式代码来确保,而多域可控性是通过使用域标签直接实现的。我们用各种艺术风格和流派验证了我们提出的关于绘画风格转移的框架。与最先进方法的定性和定量比较表明,我们的方法可以从参考风格指导或随机抽样风格中生成多领域风格和多模态实例的高质量结果。
更新日期:2021-07-22
中文翻译:
分布对齐的多模态和多域图像样式化
多模态和多域风格化是图像风格迁移领域的两个重要问题。目前,很少有方法可以同时执行多模态和多域样式化。在这项研究中,我们提出了一个多模式和多领域风格迁移的统一框架,同时支持基于示例的参考和随机抽样的指导。我们方法的关键组成部分是一种新颖的样式分布对齐模块,它消除了各种样式域之间的显式分布差距并降低了模式崩溃的风险。多模态多样性通过来自多个图像的引导或随机样式代码来确保,而多域可控性是通过使用域标签直接实现的。我们用各种艺术风格和流派验证了我们提出的关于绘画风格转移的框架。与最先进方法的定性和定量比较表明,我们的方法可以从参考风格指导或随机抽样风格中生成多领域风格和多模态实例的高质量结果。