CVDec 4, 2023

Can we truly transfer an actor's genuine happiness to avatars? An investigation into virtual, real, posed and spontaneous faces

arXiv:2312.02128v12 citationsh-index: 29SBGAMES
Originality Synthesis-oriented
AI Analysis

This work addresses the challenge of accurately transferring genuine human emotions to avatars, which is incremental but relevant for applications in education, health, entertainment, and security.

The research investigated differences in facial expression intensities between real and computer-generated (CG) faces, posed and spontaneous expressions, and across genders, finding that posed expressions show greater action unit (AU) intensities than spontaneous ones and that transforming real faces to CG reduces AU6 intensity by 80% and AU12 by 45%.

A look is worth a thousand words is a popular phrase. And why is a simple look enough to portray our feelings about something or someone? Behind this question are the theoretical foundations of the field of psychology regarding social cognition and the studies of psychologist Paul Ekman. Facial expressions, as a form of non-verbal communication, are the primary way to transmit emotions between human beings. The set of movements and expressions of facial muscles that convey some emotional state of the individual to their observers are targets of studies in many areas. Our research aims to evaluate Ekman's action units in datasets of real human faces, posed and spontaneous, and virtual human faces resulting from transferring real faces into Computer Graphics faces. In addition, we also conducted a case study with specific movie characters, such as SheHulk and Genius. We intend to find differences and similarities in facial expressions between real and CG datasets, posed and spontaneous faces, and also to consider the actors' genders in the videos. This investigation can help several areas of knowledge, whether using real or virtual human beings, in education, health, entertainment, games, security, and even legal matters. Our results indicate that AU intensities are greater for posed than spontaneous datasets, regardless of gender. Furthermore, there is a smoothing of intensity up to 80 percent for AU6 and 45 percent for AU12 when a real face is transformed into CG.

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