AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2025
Scofano, L.; Sampieri, A.; Matteis, E. De; Spinelli, I.; Galasso, F.
Social EgoMesh Estimation Proceedings Article
In: Proc. - IEEE Winter Conf. Appl. Comput. Vis., WACV, pp. 5948–5958, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833151083-1 (ISBN).
Abstract | Links | BibTeX | Tags: Augmented reality applications, Ego-motion, Egocentric view, Generative AI, Human behaviors, Human mesh recovery, Limited visibility, Recent researches, Three dimensional computer graphics, Video sequences, Virtual and augmented reality
@inproceedings{scofano_social_2025,
title = {Social EgoMesh Estimation},
author = {L. Scofano and A. Sampieri and E. De Matteis and I. Spinelli and F. Galasso},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003632729&doi=10.1109%2fWACV61041.2025.00580&partnerID=40&md5=3c2b2d069ffb596c64ee8dbc211b74a8},
doi = {10.1109/WACV61041.2025.00580},
isbn = {979-833151083-1 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Proc. - IEEE Winter Conf. Appl. Comput. Vis., WACV},
pages = {5948–5958},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Accurately estimating the 3D pose of the camera wearer in egocentric video sequences is crucial to modeling human behavior in virtual and augmented reality applications. The task presents unique challenges due to the limited visibility of the user's body caused by the front-facing camera mounted on their head. Recent research has explored the utilization of the scene and ego-motion, but it has overlooked humans' interactive nature. We propose a novel framework for Social Egocentric Estimation of body MEshes (SEE-ME). Our approach is the first to estimate the wearer's mesh using only a latent probabilistic diffusion model, which we condition on the scene and, for the first time, on the social wearer-interactee interactions. Our in-depth study sheds light on when social interaction matters most for ego-mesh estimation; it quantifies the impact of interpersonal distance and gaze direction. Overall, SEEME surpasses the current best technique, reducing the pose estimation error (MPJPE) by 53%. The code is available at SEEME. © 2025 IEEE.},
keywords = {Augmented reality applications, Ego-motion, Egocentric view, Generative AI, Human behaviors, Human mesh recovery, Limited visibility, Recent researches, Three dimensional computer graphics, Video sequences, Virtual and augmented reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Accurately estimating the 3D pose of the camera wearer in egocentric video sequences is crucial to modeling human behavior in virtual and augmented reality applications. The task presents unique challenges due to the limited visibility of the user's body caused by the front-facing camera mounted on their head. Recent research has explored the utilization of the scene and ego-motion, but it has overlooked humans' interactive nature. We propose a novel framework for Social Egocentric Estimation of body MEshes (SEE-ME). Our approach is the first to estimate the wearer's mesh using only a latent probabilistic diffusion model, which we condition on the scene and, for the first time, on the social wearer-interactee interactions. Our in-depth study sheds light on when social interaction matters most for ego-mesh estimation; it quantifies the impact of interpersonal distance and gaze direction. Overall, SEEME surpasses the current best technique, reducing the pose estimation error (MPJPE) by 53%. The code is available at SEEME. © 2025 IEEE.
2024
Dunaeva, Y.
Digital Evolution of Universities: Neural Networks in Education Book Section
In: Springer Geography, vol. Part F3974, pp. 453–463, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 2194315X (ISSN).
Abstract | Links | BibTeX | Tags: Artificial general intelligence (AGI), Cybersecurity, Cybersecurity threat, Digital exceptionalism, Dipfake, Large language models (LLM), machine learning, Virtual and augmented reality
@incollection{dunaeva_digital_2024,
title = {Digital Evolution of Universities: Neural Networks in Education},
author = {Y. Dunaeva},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216787264&doi=10.1007%2f978-3-031-70886-2_38&partnerID=40&md5=9d11413a8dc4aa487c161f0746117169},
doi = {10.1007/978-3-031-70886-2_38},
isbn = {2194315X (ISSN)},
year = {2024},
date = {2024-01-01},
booktitle = {Springer Geography},
volume = {Part F3974},
pages = {453–463},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The study articulates the main achievements and opportunities, as well as threats and potential risks of using neural networks in education. Artificial intelligence is the top research topic of 2022–2023 and the period of emergence of new terms such as large language models, dipfake, virtual and augmented reality, and so on. The paper evaluates the tremendous potential of artificial intelligence in education, among which methods such as using AI to create personalized educational programs, analyzing big data on learning success, automating the assessment and grading process, developing new teaching methods, and creating a new knowledge assessment system such as text analysis and pattern recognition. However, artificial intelligence has significantly increased risks in scientific and educational environments: the paper analyzes threats such as cyberstalking (online stalking), phishing (malicious URLs to access accounts), and others. Methodologically, the article is based on the concept of digital participation. Considering university professors as passive recipients or consumers of the services of artificial intelligence technologies, the digital inclusion strategy emphasizes only technological progress, linking it to the younger generation, and underestimates the concept of digital participation of scientists, based on systems thinking, scientific outlook, life wisdom, and devotion to moral ideals. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
keywords = {Artificial general intelligence (AGI), Cybersecurity, Cybersecurity threat, Digital exceptionalism, Dipfake, Large language models (LLM), machine learning, Virtual and augmented reality},
pubstate = {published},
tppubtype = {incollection}
}
The study articulates the main achievements and opportunities, as well as threats and potential risks of using neural networks in education. Artificial intelligence is the top research topic of 2022–2023 and the period of emergence of new terms such as large language models, dipfake, virtual and augmented reality, and so on. The paper evaluates the tremendous potential of artificial intelligence in education, among which methods such as using AI to create personalized educational programs, analyzing big data on learning success, automating the assessment and grading process, developing new teaching methods, and creating a new knowledge assessment system such as text analysis and pattern recognition. However, artificial intelligence has significantly increased risks in scientific and educational environments: the paper analyzes threats such as cyberstalking (online stalking), phishing (malicious URLs to access accounts), and others. Methodologically, the article is based on the concept of digital participation. Considering university professors as passive recipients or consumers of the services of artificial intelligence technologies, the digital inclusion strategy emphasizes only technological progress, linking it to the younger generation, and underestimates the concept of digital participation of scientists, based on systems thinking, scientific outlook, life wisdom, and devotion to moral ideals. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.