Publications

@inproceedings{paduraru_generative_2025,

title = {Generative AI for Human 3D Body Emotions: A Dataset and Baseline Methods},

author = {C. Paduraru and P. -L. Bouruc and A. Stefanescu},

editor = {Rocha A.P. and Steels L. and Herik H.J.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001951577&doi=10.5220%2f0013168700003890&partnerID=40&md5=7fa058a0c9ec8275083b55e8990a8d22},

doi = {10.5220/0013168700003890},

isbn = {21843589 (ISSN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Int. Conf. Agent. Artif. Intell.},

volume = {3},

pages = {646–653},

publisher = {Science and Technology Publications, Lda},

abstract = {Accurate and expressive representation of human emotions in 3D models remains a major challenge in various industries, including gaming, film, healthcare, virtual reality and robotics. This work aims to address this challenge by utilizing a new dataset and a set of baseline methods within an open-source framework developed to improve realism and emotional expressiveness in human 3D representations. At the center of this work is the use of a novel and diverse dataset consisting of short video clips showing people mimicking specific emotions: anger, happiness, surprise, disgust, sadness, and fear. The dataset was further processed using state-of-theart parametric body models that accurately reproduce these emotions. The resulting 3D meshes were then integrated into a generative pose generation model capable of producing similar emotions. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.},

keywords = {Animations, Body Emotions, Generative AI, Parametric Models},

pubstate = {published},

tppubtype = {inproceedings}

}