A Unified and Interpretable Emotion Representation and Expression Generation

Canonical emotions such as happy sad and fear are easy to understand and annotate. However emotions are often compound e.g. happily surprised and can be mapped to the action units (AUs) used for expressing emotions and trivially to the canonical ones. Intuitively emotions are continuous as represented by the arousal-valence (AV) model. An interpretable unification of these four modalities --namely Canonical Compound AUs and AV-- is highly desirable for a better representation and understanding of emotions. However such unification remains to be unknown in the current literature. In this work we propose an interpretable and unified emotion model referred as C2A2. We also develop a method that leverages labels of the non-unified models to annotate the novel unified one. Finally we modify the text-conditional diffusion models to understand continuous numbers which are then used to generate continuous expressions using our unified emotion model. Through quantitative and qualitative experiments we show that our generated images are rich and capture subtle expressions. Our work allows a fine-grained generation of expressions in conjunction with other textual inputs and offers a new label space for emotions at the same time.