Stroke-based rendering aims to recreate an image with a set of strokes. Most
existing methods render complex images using an uniform-block-dividing
strategy, which leads to boundary inconsistency artifacts. To solve the
problem, we propose Compositional Neural Painter, a novel stroke-based
rendering framework which dynamically predicts the next painting region based
on the current canvas, instead of dividing the image plane uniformly into
painting regions. We start from an empty canvas and divide the painting process
into several steps. At each step, a compositor network trained with a phasic RL
strategy first predicts the next painting region, then a painter network
trained with a WGAN discriminator predicts stroke parameters, and a stroke
renderer paints the strokes onto the painting region of the current canvas.
Moreover, we extend our method to stroke-based style transfer with a novel
differentiable distance transform loss, which helps preserve the structure of
the input image during stroke-based stylization. Extensive experiments show our
model outperforms the existing models in both stroke-based neural painting and
stroke-based stylization. Code is available at
this https URL

通过动态预测下一次绘画区域、预测画笔参数以及将笔触渲染到当前画布的绘画过程，提出了基于组合神经绘画家的 stroke-based rendering 框架，还借助可微分距离变换损失将该方法扩展到基于 stroke 的风格迁移，实验证明在 stroke-based neural painting 和 stroke-based stylization 方面，我们的模型优于现有模型。

基于笔画的神经绘画和风格化技术与动态预测的绘画区域

Stroke-based Neural Painting and Stylization with Dynamically Predicted  Painting Region

This paper proposes a novel stroke-based rendering (SBR) method that
translates images into vivid oil paintings. Previous SBR techniques usually
formulate the oil painting problem as pixel-wise approximation. Different from
this technique route, we treat oil painting creation as an adaptive sampling
problem. Firstly, we compute a probability density map based on the texture
complexity of the input image. Then we use the Voronoi algorithm to sample a
set of pixels as the stroke anchors. Next, we search and generate an individual
oil stroke at each anchor. Finally, we place all the strokes on the canvas to
obtain the oil painting. By adjusting the hyper-parameter maximum sampling
probability, we can control the oil painting fineness in a linear manner.
Comparison with existing state-of-the-art oil painting techniques shows that
our results have higher fidelity and more realistic textures. A user opinion
test demonstrates that people behave more preference toward our oil paintings
than the results of other methods. More interesting results and the code are in
this https URL

本文提出了一种新的基于笔画渲染（SBR）方法，将图像转换成栩栩如生的油画。通过采用自适应采样算法，基于纹理复杂度计算概率密度图，用 Voronoi 算法采样一组像素作为笔画锚点，对每个锚点搜索和生成单个油画笔画并将所有笔画放在画布上，通过调整超参数最大采样概率，可线性地控制油画的细腻程度。与现有油画技术的比较表明，该方法的结果具有更高的保真度和更逼真的纹理，用户意见测试表明，人们更喜欢我们这种油画渲染方法的结果。

Im2Oil: 基于笔画的油画渲染，通过自适应采样线性可控制细度

Im2Oil: Stroke-Based Oil Painting Rendering with Linearly Controllable  Fineness Via Adaptive Sampling

Robotic painting has been a subject of interest among both artists and
roboticists since the 1970s. Researchers and interdisciplinary artists have
employed various painting techniques and human-robot collaboration models to
create visual mediums on canvas. One of the challenges of robotic painting is
to apply a desired artistic style to the painting. Style transfer techniques
with machine learning models have helped us address this challenge with the
visual style of a specific painting. However, other manual elements of style,
i.e., painting techniques and brushstrokes of an artist, have not been fully
addressed. We propose a method to integrate an artistic style to the
brushstrokes and the painting process through collaboration with a human
artist. In this paper, we describe our approach to 1) collect brushstrokes and
hand-brush motion samples from an artist, and 2) train a generative model to
generate brushstrokes that pertains to the artist's style, and 3) fine tune a
stroke-based rendering model to work with our robotic painting setup. We will
report on the integration of these three steps in a separate publication. In a
preliminary study, 71% of human evaluators find our reconstructed brushstrokes
are pertaining to the characteristics of the artist's style. Moreover, 58% of
participants could not distinguish a painting made by our method from a
visually similar painting created by a human artist.

该论文提出了一种结合机器和人类艺术家的方法，通过生成模型和渲染模型，将艺术家的风格融入机器人绘画中，并证实了该方法的可行性和有效性。