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。