Publications

@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}

}

@inproceedings{guo_digital_2025,

title = {Digital Human Techniques for Education Reform},

author = {P. Guo and Q. Zhang and C. Tian and W. Xue and X. Feng},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001671326&doi=10.1145%2f3711403.3711428&partnerID=40&md5=dd96647315af9409d119f68f9cf4e980},

doi = {10.1145/3711403.3711428},

isbn = {979-840071746-8 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {ICETM - Proc. Int. Conf. Educ. Technol. Manag.},

pages = {173–178},

publisher = {Association for Computing Machinery, Inc},

abstract = {The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).},

keywords = {Augmented Reality, Contrastive Learning, Digital elevation model, Digital human technique, Digital Human Techniques, Digital humans, Education Reform, Education reforms, Educational Technology, Express emotions, Federated learning, Human behaviors, Human form models, Human techniques, Immersive, Innovative technology, Modeling languages, Natural language processing systems, Teachers', Teaching, Virtual environments, Virtual humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{liu_classmeta_2024,

title = {ClassMeta: Designing Interactive Virtual Classmate to Promote VR Classroom Participation},

author = {Z. Liu and Z. Zhu and L. Zhu and E. Jiang and X. Hu and K. Peppler and K. Ramani},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194868458&doi=10.1145%2f3613904.3642947&partnerID=40&md5=0592b2f977a2ad2e6366c6fa05808a6a},

doi = {10.1145/3613904.3642947},

isbn = {979-840070330-0 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Conf Hum Fact Comput Syst Proc},

publisher = {Association for Computing Machinery},

abstract = {Peer influence plays a crucial role in promoting classroom participation, where behaviors from active students can contribute to a collective classroom learning experience. However, the presence of these active students depends on several conditions and is not consistently available across all circumstances. Recently, Large Language Models (LLMs) such as GPT have demonstrated the ability to simulate diverse human behaviors convincingly due to their capacity to generate contextually coherent responses based on their role settings. Inspired by this advancement in technology, we designed ClassMeta, a GPT-4 powered agent to help promote classroom participation by playing the role of an active student. These agents, which are embodied as 3D avatars in virtual reality, interact with actual instructors and students with both spoken language and body gestures. We conducted a comparative study to investigate the potential of ClassMeta for improving the overall learning experience of the class. © 2024 Copyright held by the owner/author(s)},

keywords = {3D Avatars, Behavioral Research, Classroom learning, Collaborative learning, Computational Linguistics, Condition, E-Learning, Human behaviors, Language Model, Large language model, Learning experiences, Learning systems, pedagogical agent, Pedagogical agents, Students, Three dimensional computer graphics, Virtual Reality, VR classroom},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{liang_construction_2024,

title = {Construction of Emergency Rescue Virtual Exercise Platform Based on AIGC Perspective},

author = {J. Liang and X. Li},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206094403&doi=10.1145%2f3686424.3686477&partnerID=40&md5=e32351dc68be5d0fa0d771656b02256f},

doi = {10.1145/3686424.3686477},

isbn = {979-840071036-0 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {ACM Int. Conf. Proc. Ser.},

pages = {312–316},

publisher = {Association for Computing Machinery},

abstract = {In order to address the suddenness of emergency events and the phenomenon that the rescue process contains too many behavioural uncertainties, an emergency rescue virtual exercise platform framework has been designed from the perspective of generative artificial intelligence (AIGC). This framework analyses human behaviour during the simulated emergency rescue process and collects relevant data. The module function is determined by the parallel emergency management method. The system comprises three data processing modules: the behavioural input module, the emergency event feedback module, and the data classification and processing module. The logic of AI data processing is employed to establish a data cycle evolution system, which assists rescue personnel in enhancing their professional abilities, increasing the success rate of rescue operations, and optimising the role of AI technology and computer simulation methodology in the design of the practice. © 2024 Copyright held by the owner/author(s).},

keywords = {Artificial intelligence, Behavioral theory, Data handling, Data Processing, Emergency events, Emergency management, Emergency rescue, Emergency Response, Human behaviors, Processing modules, Rescue process, Risk management, Uncertainty, Virtual environments, Virtual exercise, Virtual Exercises, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{wang_decentralized_2022,

title = {Decentralized, not Dehumanized in the Metaverse: Bringing Utility to NFTs through Multimodal Interaction},

author = {A. Wang and Z. Gao and L. H. Lee and T. Braud and P. Hui},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142799074&doi=10.1145%2f3536221.3558176&partnerID=40&md5=f9dee1e9e60afc71c4533cbdee0b98a7},

doi = {10.1145/3536221.3558176},

isbn = {978-145039390-4 (ISBN)},

year  = {2022},

date = {2022-01-01},

booktitle = {ACM Int. Conf. Proc. Ser.},

pages = {662–667},

publisher = {Association for Computing Machinery},

abstract = {User Interaction for NFTs (Non-fungible Tokens) is gaining increasing attention. Although NFTs have been traditionally single-use and monolithic, recent applications aim to connect multimodal interaction with human behavior. This paper reviews the related technological approaches and business practices in NFT art. We highlight that multimodal interaction is a currently under-studied issue in mainstream NFT art, and conjecture that multimodal interaction is a crucial enabler for decentralization in the NFT community. We present a continuum theory and propose a framework combining a bottom-up approach with AI multimodal process. Through this framework, we put forward integrating human behavior data into generative NFT units, as "multimodal interactive NFT."Our work displays the possibilities of NFTs in the art world, beyond the traditional 2D and 3D static content. © 2022 ACM.},

keywords = {AI-generated art, Arts computing, Behavioral Research, Computation theory, Continuum mechanics, Decentralised, Human behaviors, Interaction, Multi-modal, multimodal, Multimodal Interaction, NFTs, Non-fungible token, Text-to-image, The metaverse},

pubstate = {published},

tppubtype = {inproceedings}

}