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 https://github.com/TZYSJTU/Im2Oil.

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