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Recently deep generative models have achieved impressive results in the fieldof automated facial expression editing. However, the approaches presented sofar presume a discrete representation of human emotions and are thereforelimited in the modelling of non-discrete emotional expressions. To overcomethis limitation, we explore how continuous emotion representations can be usedto control automated expression editing. We propose a deep generative modelthat can be used to manipulate facial expressions in facial images according tocontinuous two-dimensional emotion labels. One dimension represents anemotion's valence, the other represents its degree of arousal. We demonstratethe functionality of our model with a quantitative analysis using classifiernetworks as well as with a qualitative analysis.

Facial Expression Editing with Continuous Emotion Labels