The Fast Style Transfer methods have been recently proposed to transfer a
photograph to an artistic style in real-time. This task involves controlling
the stroke size in the stylized results, which remains an open challenge. In
this paper, we present a stroke controllable style transfer network that can
achieve continuous and spatial stroke size control. By analyzing the factors
that influence the stroke size, we propose to explicitly account for the
receptive field and the style image scales. We propose a StrokePyramid module
to endow the network with adaptive receptive fields, and two training
strategies to achieve faster convergence and augment new stroke sizes upon a
trained model respectively. By combining the proposed runtime control
strategies, our network can achieve continuous changes in stroke sizes and
produce distinct stroke sizes in different spatial regions within the same
output image.

本研究提供了一种可以进行实时连续笔画大小变化的笔画可控艺术风格转移网络，并通过使用自适应感受野和两种训练策略以实现更快的收敛和新增笔画大小。通过组合所提出的运行时控制策略，该网络可以实现笔画大小的连续变化，并在同一输出图像的不同空间区域中产生不同的笔画大小。

自适应感受野的可控笔画快速风格迁移

Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields

In this paper, we present a method which combines the flexibility of the
neural algorithm of artistic style with the speed of fast style transfer
networks to allow real-time stylization using any content/style image pair. We
build upon recent work leveraging conditional instance normalization for
multi-style transfer networks by learning to predict the conditional instance
normalization parameters directly from a style image. The model is successfully
trained on a corpus of roughly 80,000 paintings and is able to generalize to
paintings previously unobserved. We demonstrate that the learned embedding
space is smooth and contains a rich structure and organizes semantic
information associated with paintings in an entirely unsupervised manner.

本文提出一种方法，将艺术风格的神经算法的灵活性与快速风格转移网络的速度相结合，利用任意内容 / 风格图像对进行实时样式化。通过学习直接从样式图像预测条件实例归一化参数，在多风格转移网络中使用条件实例归一化对最近工作进行了改进。该模型成功地在大约 8 万幅绘画作品上进行了训练，并能够推广到以前未观察到的作品。我们证明，学习到的嵌入空间是平滑的，并在整个非监督学习过程中包含了与绘画相关的丰富结构和组织语义信息。