A Unified and Interpretable Emotion Representation and Expression Generation

Canonical emotions, such as happy, sad, and fearful, are easy to understand and annotate. However, emotions are of-ten 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 in-terpretable 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 under-stand 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 ex-pressions. Our work allows a fine- grained generation of ex-pressions in conjunction with other textual inputs and offers a new label space for emotions at the same time.