Publications

@inproceedings{samson_fostering_2024,

title = {Fostering a Co-creation Process for the Development of an Extended Reality Healthcare Education Resource},

author = {J. Samson and P. Lameras and N. Taylor and R. Kneafsey},

editor = {Auer M.E. and Tsiatsos T.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189759614&doi=10.1007%2f978-3-031-56075-0_20&partnerID=40&md5=6ae832882a2e224094c1beb81c925333},

doi = {10.1007/978-3-031-56075-0_20},

isbn = {23673370 (ISSN); 978-303156074-3 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Lect. Notes Networks Syst.},

volume = {937 LNNS},

pages = {205–212},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The aim of this research is to create an immersive healthcare education resource using an extended reality (XR) platform. This platform leverages an existing software product, incorporating virtual patients with conversational capabilities driven by artificial intelligence (AI). The initial stage produced an early prototype focused on assessing an elderly virtual patient experiencing frailty. This scenario encompasses the hospital admission to post-discharge care at home, involving various healthcare professionals such as paramedics, emergency clinicians, diagnostic radiographers, geriatricians, physiotherapists, occupational therapists, nurses, operating department practitioners, dietitians, and social workers. The plan moving forward is to refine and expand this prototype through a co-creation with diverse stakeholders. The refinement process will include the introduction of updated scripts into the standard AI model. Furthermore, these scripts will be tested against a new hybrid model that combines generative AI. Ultimately, this resource will be co-designed to create a learning activity tailored for occupational therapy and physiotherapy students. This activity will undergo testing with a cohort of students, and the outcomes of this research are expected to inform the future development of interprofessional virtual simulated placements (VSPs). These placements will complement traditional clinical learning experiences, offering students an immersive environment to enhance their skills and knowledge in the healthcare field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},

keywords = {Artificial intelligence, Co-creation, Creation process, Diagnosis, Education computing, Education resource, Extended reality, Health care education, Hospitals, Immersive, Inter professionals, Interprofessional Healthcare Education, Software products, Students, Virtual patients},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{weng_studying_2024,

title = {Studying How Prompt-Generated 3D Models Affect the Creation Process of Mixed Reality Applications},

author = {S. C. -C. Weng},

editor = {Eck U. and Sra M. and Stefanucci J. and Sugimoto M. and Tatzgern M. and Williams I.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214403987&doi=10.1109%2fISMAR-Adjunct64951.2024.00196&partnerID=40&md5=46d553927e96356d73ffc5996fbbdc71},

doi = {10.1109/ISMAR-Adjunct64951.2024.00196},

isbn = {979-833150691-9 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. - IEEE Int. Symp. Mixed Augment. Real. Adjunct, ISMAR-Adjunct},

pages = {654–655},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {In this doctoral consortium, we build upon our previous research prototype, Dream Mesh, a Mixed Reality application that generates models in MR based on user speech prompts. To evaluate the application and answer the questions derived from our pilot research, I propose a future user study plan. This plan aims to investigate how prompt-generated 3D models affect the creation process of Mixed Reality applications. © 2024 IEEE.},

keywords = {3D modeling, 3D models, 3d-modeling, Creation process, Generative AI, Mixed reality, Prompt-generated 3d model, Prompt-generated 3D models, Research prototype, Study plans, User study},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{martini_semi_2024,

title = {Semi -Automated Digital Human Production for Enhanced Media Broadcasting},

author = {M. Martini and V. Valentini and A. Ciprian and A. Bottino and R. Iacoviello and M. Montagnuolo and A. Messina and F. Strada and D. Zappia},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199536742&doi=10.1109%2fGEM61861.2024.10585601&partnerID=40&md5=3703fba931b02f9615316db8ebbca70c},

doi = {10.1109/GEM61861.2024.10585601},

isbn = {979-835037453-7 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {IEEE Gaming, Entertain., Media Conf., GEM},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {In recent years, the application of synthetic humans in various fields has attracted considerable attention, leading to extensive exploration of their integration into the Metaverse and virtual production environments. This work presents a semi-automated approach that aims to find a fair trade-off between high-quality outputs and efficient production times. The project focuses on the Rai photo and video archives to find images of target characters for texturing and 3D reconstruction with the goal of reviving Rai's 2D footage and enhance the media experience. A key aspect of this study is to minimize the human intervention, ensuring an efficient, flexible, and scalable creation process. In this work, the improvements have been distributed among different stages of the digital human creation process, starting with the generation of 3D head meshes from 2D images of the reference character and then moving on to the generation, using a Diffusion model, of suitable images for texture development. These assets are then integrated into the Unreal Engine, where a custom widget facilitates posing, rendering, and texturing of Synthetic Humans models. Finally, an in-depth quantitative comparison and subjective tests were carried out between the original character images and the rendered synthetic humans, confirming the validity of the approach. © 2024 IEEE.},

keywords = {AI automation, Automation, Creation process, Digital humans, Economic and social effects, Extensive explorations, Face reconstruction, Generative AI, Image enhancement, media archive, Media archives, Metaverses, Rendering (computer graphics), Synthetic human, Synthetic Humans, Textures, Three dimensional computer graphics, Virtual production, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{vincent_roblox_2023,

title = {Roblox Generative AI in action},

author = {B. Vincent and K. Ayyar},

editor = {Spencer S.N.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167946022&doi=10.1145%2f3588430.3597250&partnerID=40&md5=61fda81c33eb3623240f7d14f51607b0},

doi = {10.1145/3588430.3597250},

isbn = {979-840070158-0 (ISBN)},

year  = {2023},

date = {2023-01-01},

booktitle = {Proc. - SIGGRAPH Real-Time Live!},

publisher = {Association for Computing Machinery, Inc},

abstract = {Roblox is investing in generative AI techniques to revolutionize the creation process on its platform. By leveraging natural language and other intuitive expressions of intent, creators can build interactive objects and scenes without complex modeling or coding. The use of AI image generation services and large language models aim to make creation faster and easier for every user on the platform. © 2023 Owner/Author.},

keywords = {AI techniques, Complex model, Creation process, Education, Game, Games, Interactive computer graphics, Interactive objects, Lighting, Metaverse, Metaverses, Modeling, Modeling languages, Natural languages, Object and scenes, Pipeline, Real-Time Rendering, Rendering (computer graphics)},

pubstate = {published},

tppubtype = {inproceedings}

}