Canonical emotions, such as happy, sad, and fearful, 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.

我们提出了一个可解释且统一的情感模型 (C2A2)，并通过修改文本条件扩散模型来生成连续表情，从而实现了情感的精细生成。