论文标题
使用分离功能进行照片真实风格的转移
Using Decoupled Features for Photo-realistic Style Transfer
论文作者
论文摘要
在这项工作中，我们提出了一种基于视觉科学原理以及用于样本统计确定性解耦的最新数学表述的图像和视频的感性风格转移方法。我们方法的新方面包括匹配更高顺序的脱钩时刻，而不是公共样式转移方法，以及匹配功率谱的描述符，以表征和传递源和目标之间的扩散效应，这是文献中未曾考虑过的东西。结果具有很高的视觉质量，没有时空伪像，并且以观察者偏好实验的形式进行验证测试表明，我们的方法与最新的方法很好地比较。该算法的计算复杂性很低，我们提出了一个数值实现，该实现适用于实时视频应用程序。最后，我们工作的另一个贡献是指出，当前的感性逼真风格转移的深度学习方法并没有真正在有限的示例之外实现逼真的质量，因为结果常常表现出不可接受的视觉文物。
In this work we propose a photorealistic style transfer method for image and video that is based on vision science principles and on a recent mathematical formulation for the deterministic decoupling of sample statistics. The novel aspects of our approach include matching decoupled moments of higher order than in common style transfer approaches, and matching a descriptor of the power spectrum so as to characterize and transfer diffusion effects between source and target, which is something that has not been considered before in the literature. The results are of high visual quality, without spatio-temporal artifacts, and validation tests in the form of observer preference experiments show that our method compares very well with the state-of-the-art. The computational complexity of the algorithm is low, and we propose a numerical implementation that is amenable for real-time video application. Finally, another contribution of our work is to point out that current deep learning approaches for photorealistic style transfer don't really achieve photorealistic quality outside of limited examples, because the results too often show unacceptable visual artifacts.
