  With the advancement of deep learning, artificial intelligence (AI) has made
many breakthroughs in recent years and achieved superhuman performance in
various tasks such as object detection, reading comprehension, and video games.
Generative Modeling, such as various Generative Adversarial Networks (GAN)
models, has been applied to generate paintings and music. Research in Natural
Language Processing (NLP) also had a leap forward in 2018 since the release of
the pre-trained contextual neural language models such as BERT and recently
released GPT3. Despite the exciting AI applications aforementioned, AI is still
significantly lagging behind humans in creativity, which is often considered
the ultimate moonshot for AI. Our work is inspired by Chinese calligraphy,
which is a unique form of visual art where the character itself is an aesthetic
painting. We also draw inspirations from paintings of the Abstract
Expressionist movement in the 1940s and 1950s, such as the work by American
painter Franz Kline. In this paper, we present a creative framework based on
Conditional Generative Adversarial Networks and Contextual Neural Language
Model to generate abstract artworks that have intrinsic meaning and aesthetic
value, which is different from the existing work, such as image captioning and
text-to-image generation, where the texts are the descriptions of the images.
In addition, we have publicly released a Chinese calligraphy image dataset and
demonstrate our framework using a prototype system and a user study.
