论文标题
分布对齐的多模式和多域图像样式
Distribution Aligned Multimodal and Multi-Domain Image Stylization
论文作者
论文摘要
在图像样式传输领域，多模式和多域样式化是两个重要问题。当前，很少有可以同时执行多模式和多域样式的方法。在本文中，我们在基于示例的参考和随机采样指导的支持下，提出了一个多模式和多域样式转移的统一框架。我们方法的关键组成部分是一种新型的样式分布对齐模块，该模块消除了各种样式域之间的明确分布差距，并降低了模式崩溃的风险。通过多个图像或随机样式代码的指导来确保多模式多样性，而多域可控性是直接通过使用域标签来实现的。我们通过各种不同的艺术风格和流派来验证我们提出的绘画风格转移框架。与最先进的方法进行的定性和定量比较表明，我们的方法可以通过参考样式指导或随机采样样式生成多域样式和多模式实例的高质量结果。
Multimodal and multi-domain stylization are two important problems in the field of image style transfer. Currently, there are few methods that can perform both multimodal and multi-domain stylization simultaneously. In this paper, 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 code, while the multi-domain controllability is directly achieved by using a domain label. We validate our proposed framework on painting style transfer with a variety of different 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 with reference style guidance or random sampled style.
