arXiv subjects

Vitor Miguel Xavier Peres

Publications and source records attributed to Vitor Miguel Xavier Peres.

2 recordsLinked to original sources

arXiv2026

Emotion Intensity Matters: Generating Realistic Expressions in Virtual Humans with CVAEs

Generating expressive facial behavior in virtual humans (VHs) remains a central challenge in affective computing and character animation. This paper presents a novel approach based on Conditional Variational Autoencoders (CVAEs), trained on real human facial expression data, to synthesize controllable emotional expressions at varying intensities. Using a dataset comprising six basic emotions represented at two intensity levels (low and high), we train a CVAE model to generate synthetic facial expression data while preserving semantic consistency with real human expressions. Despite the limited amount of training data (only 7,680 facial expression samples), the proposed approach learns meaningful latent representations and generates coherent emotional variations. Our method enables control over emotional intensity, making it suitable for animating virtual characters without requiring actor performances or manual artistic intervention. Our research aimed to evaluate whether the method (CVAE) preserves the characteristics associated with the different intensity levels present in the dataset. Results show that the proposed model preserves key expressive characteristics across intensity levels while supporting generalization across emotional intensity levels, contributing to the creation of emotionally expressive virtual characters from relatively small datasets.

cs.CV↗

arXiv2023

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

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.

cs.CV↗