  This paper introduces a novel method for generating artistic images that
express particular affective states. Leveraging state-of-the-art deep learning
methods for visual generation (through generative adversarial networks),
semantic models from OpenAI, and the annotated dataset of the visual art
encyclopedia WikiArt, our AffectGAN model is able to generate images based on
specific or broad semantic prompts and intended affective outcomes. A small
dataset of 32 images generated by AffectGAN is annotated by 50 participants in
terms of the particular emotion they elicit, as well as their quality and
novelty. Results show that for most instances the intended emotion used as a
prompt for image generation matches the participants' responses. This
small-scale study brings forth a new vision towards blending affective
computing with computational creativity, enabling generative systems with
intentionality in terms of the emotions they wish their output to elicit.
