Facial Expression Editing with Continuous Emotion Labels
arXiv:2006.12210
Abstract
Recently deep generative models have achieved impressive results in the field of automated facial expression editing. However, the approaches presented so far presume a discrete representation of human emotions and are therefore limited in the modelling of non-discrete emotional expressions. To overcome this limitation, we explore how continuous emotion representations can be used to control automated expression editing. We propose a deep generative model that can be used to manipulate facial expressions in facial images according to continuous two-dimensional emotion labels. One dimension represents an emotion's valence, the other represents its degree of arousal. We demonstrate the functionality of our model with a quantitative analysis using classifier networks as well as with a qualitative analysis.
arXiv comment8 pages, 5 figures. 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019), May 2019
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