In the study of generative art, it is relatively easy at present to achieve a high degree of certainty or uncertainty. However, the combination of certainty and uncertainty has always been an area of difficulty in generative art. In this paper, we present a novel FusionGAN system to automate the generation of abstract paintings. These generated abstract paintings combine the factors of certainty and uncertainty. First, we collect an APdataset consisting of three parts: abstract paintings drawn by artists, sketches, and abstract paintings generated by other neural network methods. We then train the proposed FusionGAN system on the collected dataset to learn the expression of abstract paintings. Corresponding to the two-step operation of the combination of certainty and uncertainty in the artist’s creation, the proposed FusionGAN system is also divided into two steps for the generation of abstract paintings. More specifically, the first step is the basic structure establishment, which corresponds to the fundamental certainty element in the painting creation. The second step is the realization of details, which integrates the uncertain details based on the basic structure. The experimental results achieved by our system in abstract painting generation enrich the diversity of artistic creation and have been recognized by art institutions, with some results displayed on their websites.

在生成艺术的研究中，目前比较容易达到高度的确定性或不确定性。然而，确定性和不确定性的结合一直是生成艺术的难点。在本文中，我们提出了一种新颖的 FusionGAN 系统来自动生成抽象画。这些生成的抽象画结合了确定性和不确定性因素。首先，我们收集了一个由三部分组成的APdataset：艺术家绘制的抽象画、素描和其他神经网络方法生成的抽象画。然后，我们在收集的数据集上训练提出的 FusionGAN 系统，以学习抽象绘画的表达。对应艺术家创作中确定性与不确定性结合的两步操作，提出的 FusionGAN 系统也分为两个步骤来生成抽象画。更具体地说，第一步是基本结构的建立，它对应于绘画创作中的基本确定性要素。第二步是细节的实现，在基本结构的基础上整合不确定的细节。我们的系统在抽象绘画生成方面取得的实验成果丰富了艺术创作的多样性，得到了艺术机构的认可，部分成果已在其网站上展示。它在基本结构的基础上整合了不确定的细节。我们的系统在抽象绘画生成方面取得的实验成果丰富了艺术创作的多样性，得到了艺术机构的认可，部分成果已在其网站上展示。它在基本结构的基础上整合了不确定的细节。我们的系统在抽象绘画生成方面取得的实验成果丰富了艺术创作的多样性，得到了艺术机构的认可，部分成果已在其网站上展示。