Affective Image Filter: Reflecting Emotions from Text to Images

Understanding the emotions in text and presenting them visually is a very challenging problem that requires a deep understanding of natural language and high-quality image synthesis simultaneously. In this work, we propose Affective Image Filter (AIF), a novel model that is able to understand the visually-abstract emotions from the text and reflect them to visually-concrete images with appropriate colors and textures. We build our model based on the multi-modal transformer architecture, which unifies both images and texts into tokens and encodes the emotional prior knowledge. Various loss functions are proposed to understand complex emotions and produce appropriate visualization. In addition, we collect and contribute a new dataset with abundant aesthetic images and emotional texts for training and evaluating the AIF model. We carefully design four quantitative metrics and conduct a user study to comprehensively evaluate the performance, which demonstrates our AIF model outperforms state-of-the-art methods and could evoke specific emotional responses from human observers.