Publications

@inproceedings{koizumi_development_2025,

title = {Development and Assessment of a System to Help Students Improve Self-compassion},

author = {M. Koizumi and M. Ohsuga and J. M. Corchado},

editor = {Chinthaginjala R. and Sitek P. and Min-Allah N. and Matsui K. and Ossowski S. and Rodríguez S.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218979175&doi=10.1007%2f978-3-031-82073-1_5&partnerID=40&md5=b136d4a114ce5acfa89f907ccecc145f},

doi = {10.1007/978-3-031-82073-1_5},

isbn = {23673370 (ISSN); 978-303182072-4 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Lect. Notes Networks Syst.},

volume = {1259},

pages = {43–52},

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

abstract = {Mental health issues are becoming more prevalent among university students. The mindful self-compassion (MSC) training program, which was introduced to address this issue, has shown some efficacy. However, many people, particularly Japanese people, have difficulty recalling visual imagery or feel uncomfortable or resistant to treating themselves with compassion. This study proposes and develops a system that uses virtual space and avatars to help individuals improve their self-compassion. In the proposed system, the user first selects an avatar of a person with whom to talk (hereafter referred to as “partner”), and then talks about the problem to the avatar of his/her choice. Next, the user changes viewpoints and listens to the problem as the partner’s avatar and responds with compassion. Finally, the user returns to his/her own avatar and listens to the compassionate response spoken as the partner avatar. We first conducted surveys to understand the important system components, and then developed prototypes. In light of the results of the experiments, we improved the prototype by introducing a generative AI. The first prototype used the user’s spoken voice as it was, but the improved system uses the generative AI to organize and convert the voice and present it. In addition, we added a function to generate and add advice with compression. The proposed system is expected to contribute to the improvement of students’ self-compassion. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

keywords = {Avatar, Generative adversarial networks, Generative AI, Health issues, Mental health, Self-compassion, Students, Training program, University students, Virtual avatar, Virtual environments, Virtual Reality, Virtual Space, Virtual spaces, Visual imagery},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sajiukumar_generative_2025,

title = {Generative AI-Enabled Virtual Twin for Meeting Assistants},

author = {A. Sajiukumar and A. Ranjan and P. K. Parvathi and A. Satheesh and J. Divya Udayan and U. Subramaniam},

editor = {Saba T. and Rehman A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007691247&doi=10.1109%2fWiDS-PSU64963.2025.00025&partnerID=40&md5=f0bfb74a8f854c427054c73582909185},

doi = {10.1109/WiDS-PSU64963.2025.00025},

isbn = {979-833152092-2 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Proc. - Int. Women Data Sci. Conf. at Prince Sultan Univ., WiDS-PSU},

pages = {60–65},

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

abstract = {The growing dependence on virtual spaces for communication and collaboration has transformed interactions in numerous industries, ranging from professional meetings to education, entertainment, and healthcare. Despite the advancement of AI technologies such as three-dimensional modeling, voice cloning, and conversational AI, the convergence of these technologies in a single platform is still challenging. This paper introduces a unified framework that brings together state-of-the-art 3D avatar generation, real-time voice cloning, and conversational AI to enhance virtual interactions. The system utilizes Triplane neural representations and neural radiation fields (NeRF) for high-fidelity 3D avatar generation, speaker encoders coupled with Tacotron 2 and WaveRNN for natural voice cloning, and a context-aware chat algorithm for adaptive conversations. By overcoming the challenges of customization, integration, and real-time performance, the proposed framework addresses the increasing needs for realistic virtual representations, setting new benchmarks for AI-augmented interaction in virtual conferences, online representation, education, and healthcare. © 2025 IEEE.},

keywords = {3D avatar generation, 3D Avatars, 3D reconstruction, AI-augmented interaction, Augmented Reality, Communication and collaborations, Conversational AI, Neural radiation field, neural radiation fields (NeRF), Radiation field, Real time performance, real-time performance, Three dimensional computer graphics, Virtual spaces, Voice cloning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{geetha_human_2024,

title = {Human Interaction in Virtual and Mixed Reality Through Hand Tracking},

author = {S. Geetha and G. Aditya and M. Chetan Reddy and G. Nischith},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205768661&doi=10.1109%2fCONECCT62155.2024.10677239&partnerID=40&md5=173e590ca9a1e30b760d05af562f311a},

doi = {10.1109/CONECCT62155.2024.10677239},

isbn = {979-835038592-2 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. CONECCT - IEEE Int. Conf. Electron., Comput. Commun. Technol.},

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

abstract = {This paper explores the potential and possibilities of hand tracking in virtual reality (VR) and mixed reality (MR), focusing on its role in human interaction dynamics. An application was designed in Unity leveraging the XR Interaction toolkit, within which various items across three important domains: daily life, education, and recreation, were crafted to demonstrate the versatility of hand tracking along with hand gesture-based shortcuts for interaction. Integration of elements in MR ensures that users can seamlessly enjoy virtual experiences while remaining connected to their physical surroundings. Precise hand tracking enables effortless interaction with the virtual space, enhancing presence and control with a user-friendly interface. Additionally, the paper explores the effectiveness of integrating hand tracking into education and training scenarios. A computer assembly simulation was created to demonstrate this, featuring component inspection and zoom capabilities along with a large language model (LLM) integrated with hand gestures to provide for interaction capabilities. © 2024 IEEE.},

keywords = {Computer interaction, Computer simulation languages, Daily lives, Digital elevation model, Hand gesture, hand tracking, Hand-tracking, human-computer interaction, Humaninteraction, Interaction dynamics, Mixed reality, Unity, User friendly interface, User interfaces, Virtual environments, Virtual Reality, Virtual spaces},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{yang_prompt_2024,

title = {From Prompt to Metaverse: User Perceptions of Personalized Spaces Crafted by Generative AI},

author = {S. Yang and Y. H. Tsui and X. Wang and A. Alhilal and R. H. Mogavi and X. Wang and P. Hui},

editor = {Bernstein M. and Bruckman A. and Gadiraju U. and Halfaker A. and Ma X. and Pinatti F. and Redi M. and Ribes D. and Savage S. and Zhang A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214583858&doi=10.1145%2f3678884.3681897&partnerID=40&md5=c914b32a0cee1520712062e6ec35eb3a},

doi = {10.1145/3678884.3681897},

isbn = {979-840071114-5 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. ACM Conf. Comput. Support. Coop. Work CSCW},

pages = {497–504},

publisher = {Association for Computing Machinery},

abstract = {Generative artificial intelligence (AI) has revolutionized content creation. In parallel, the Metaverse has emerged to transcend the constraints of our physical reality. While Generative AI has a multitude of exciting applications for the fields of writing, coding, and graphic design, its usage to personalize our virtual space has not yet been explored. In this paper, we investigate the application of Artificial Intelligence Generated Content (AIGC) to personalize our virtual spaces and enhance the metaverse experience. To this end, we present a pipeline to enable users to customize their virtual spaces. Moreover, we explore the hardware resources and latency required for personalized spaces, as well as user acceptance of the AI-generated spaces. Comprehensive user studies follow extensive system experiments. Our research evaluates users' perceptions of two generated spaces: panoramic images and 3D virtual spaces. According to our findings, users have shown a great interest in 3D personalized spaces, and the practicality and immersion of 3D space generation tools surpass panoramic space generation tools. © 2024 ACM.},

keywords = {AI-generated content, Content creation, Generation tools, Generative adversarial networks, generative artificial intelligence, HCI, Metaverse, Metaverses, Personalization, Personalizations, Space generation, User perceptions, Virtual environments, Virtual Reality, Virtual spaces},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{numan_spaceblender_2024,

title = {SpaceBlender: Creating Context-Rich Collaborative Spaces Through Generative 3D Scene Blending},

author = {N. Numan and S. Rajaram and B. T. Kumaravel and N. Marquardt and A. D. Wilson},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209252034&doi=10.1145%2f3654777.3676361&partnerID=40&md5=8744057832f9098eabfd16c8b2b5fe62},

doi = {10.1145/3654777.3676361},

isbn = {979-840070628-8 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {UIST - Proc. Annual ACM Symp. User Interface Softw. Technol.},

publisher = {Association for Computing Machinery, Inc},

abstract = {There is increased interest in using generative AI to create 3D spaces for Virtual Reality (VR) applications. However, today's models produce artificial environments, falling short of supporting collaborative tasks that benefit from incorporating the user's physical context. To generate environments that support VR telepresence, we introduce SpaceBlender, a novel pipeline that utilizes generative AI techniques to blend users' physical surroundings into unified virtual spaces. This pipeline transforms user-provided 2D images into context-rich 3D environments through an iterative process consisting of depth estimation, mesh alignment, and diffusion-based space completion guided by geometric priors and adaptive text prompts. In a preliminary within-subjects study, where 20 participants performed a collaborative VR affinity diagramming task in pairs, we compared SpaceBlender with a generic virtual environment and a state-of-the-art scene generation framework, evaluating its ability to create virtual spaces suitable for collaboration. Participants appreciated the enhanced familiarity and context provided by SpaceBlender but also noted complexities in the generative environments that could detract from task focus. Drawing on participant feedback, we propose directions for improving the pipeline and discuss the value and design of blended spaces for different scenarios. © 2024 ACM.},

keywords = {3D modeling, 3D scenes, 3D spaces, AI techniques, Artificial environments, Collaborative spaces, Collaborative tasks, Generative adversarial networks, Generative AI, Telepresence, Virtual environments, Virtual Reality, Virtual reality telepresence, Virtual spaces, VR telepresence},

pubstate = {published},

tppubtype = {inproceedings}

}