Creating meaningful art is often viewed as a uniquely human endeavor. A human
artist needs a combination of unique skills, understanding, and genuine
intention to create artworks that evoke deep feelings and emotions. In this
paper, we introduce a multi-conditional Generative Adversarial Network (GAN)
approach trained on large amounts of human paintings to synthesize
realistic-looking paintings that emulate human art. Our approach is based on
the StyleGAN neural network architecture, but incorporates a custom
multi-conditional control mechanism that provides fine-granular control over
characteristics of the generated paintings, e.g., with regard to the perceived
emotion evoked in a spectator. For better control, we introduce the conditional
truncation trick, which adapts the standard truncation trick for the
conditional setting and diverse datasets. Finally, we develop a diverse set of
evaluation techniques tailored to multi-conditional generation.

本文介绍了一种基于生成对抗网络的多条件控制方法，通过对大量人类绘画进行训练，生成模拟人类艺术的逼真画作，提供了对生成画作的特征进行精细控制，针对多条件生成，引入了条件截断技巧，并使用一系列评估技术对生成结果进行定量分析。