Multimodal Interaction System Supported by Digital Humans
Notice bibliographique
Résumé
As digital services continue to grow in popularity, there is an increasing need for systems that replicate human interaction while prioritizing user satisfaction and engagement. Early implementations offered interactions that felt unnatural, mainly due to the lack of realistic facial expressions and movement in character animations. Over time, however, significant advancements—particularly from the video game industry—have driven major improvements in this field. Titles such as Senua’s Saga: Hellblade II and Black Myth: Wukong illustrate how these developments have enabled the creation of immersive characters with highly realistic facial animation. The Metaverse has emerged as a key area of interest, offering immersive virtual environments where users interact within a shared digital space. This evolution has increased the demand for personalized Digital Humans—high-fidelity, computer-generated avatars capable of expressing empathy in real time. This study examines how Digital Humans can enhance human–computer interaction by reducing the emotional disconnect commonly associated with automated systems. Such avatars show potential across remote meetings, customer service, online education, and Metaverse platforms, fostering more natural and engaging interactions. Emotionally expressive Digital Humans were created using the MetaHuman framework and Unreal Engine, incorporating multimodal MoCap based on computer vision and RGB camera input. Two user tests were conducted: one focused on facial expressions and another combining facial expressions with full-body movement, involving a total of 40 participants. Empathy levels were assessed using the Toronto Empathy Questionnaire (TEQ), administered before and after interaction. One-Way ANOVA analyses showed no statistically significant differences in elicited empathy between default and custom animations. In the facial-animation test, participants’ average TEQ scores were 47.4 for default animations and 45.0 for custom ones, both within or above the general population average (40–45). In the combined full-body test, mean scores were 47.6 for human body movement and 45.5 for MetaHuman body motion. Although personalization did not significantly outperform default animations, the results highlight the essential role of realistic body movement in shaping emotional perception and interaction quality. The findings confirm that emotionally expressive Digital Humans can be effectively integrated into digital platforms, while showing that facial personalization alone must be complemented by contextual and narrative elements to maximize empathetic impact. This work provides a solid foundation for future research on deploying Digital Humans in real-world interactive systems.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,007 | 0,007 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».