AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
How to
Here you can find the complete list of our publications.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2024
Si, J.; Yang, S.; Song, J.; Son, S.; Lee, S.; Kim, D.; Kim, S.
Generating and Integrating Diffusion Model-Based Panoramic Views for Virtual Interview Platform Proceedings Article
In: IEEE Int. Conf. Artif. Intell. Eng. Technol., IICAIET, pp. 343–348, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835038969-2 (ISBN).
Abstract | Links | BibTeX | Tags: AI, Deep learning, Diffusion, Diffusion Model, Diffusion technology, Digital elevation model, High quality, Manual process, Model-based OPC, New approaches, Panorama, Panoramic views, Virtual environments, Virtual Interview, Virtual Reality
@inproceedings{si_generating_2024,
title = {Generating and Integrating Diffusion Model-Based Panoramic Views for Virtual Interview Platform},
author = {J. Si and S. Yang and J. Song and S. Son and S. Lee and D. Kim and S. Kim},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209663031&doi=10.1109%2fIICAIET62352.2024.10730450&partnerID=40&md5=a52689715ec912c54696948c34fc0263},
doi = {10.1109/IICAIET62352.2024.10730450},
isbn = {979-835038969-2 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {IEEE Int. Conf. Artif. Intell. Eng. Technol., IICAIET},
pages = {343–348},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {This paper presents a new approach to improve virtual interview platforms in education, which are gaining significant attention. This study aims to simplify the complex manual process of equipment setup to enhance the realism and reliability of virtual interviews. To this end, this study proposes a method for automatically constructing 3D virtual interview environments using diffusion technology in generative AI. In this research, we exploit a diffusion model capable of generating high-quality panoramic images. We generate images of interview rooms capable of delivering immersive interview experiences via refined text prompts. The resulting imagery is then reconstituted 3D VR content utilizing the Unity engine, facilitating enhanced interaction and engagement within virtual environments. This research compares and analyzes various methods presented in related research and proposes a new process for efficiently constructing 360-degree virtual environments. When wearing Oculus Quest 2 and experiencing the virtual environment created using the proposed method, a high sense of immersion was experienced, similar to the actual interview environment. © 2024 IEEE.},
keywords = {AI, Deep learning, Diffusion, Diffusion Model, Diffusion technology, Digital elevation model, High quality, Manual process, Model-based OPC, New approaches, Panorama, Panoramic views, Virtual environments, Virtual Interview, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a new approach to improve virtual interview platforms in education, which are gaining significant attention. This study aims to simplify the complex manual process of equipment setup to enhance the realism and reliability of virtual interviews. To this end, this study proposes a method for automatically constructing 3D virtual interview environments using diffusion technology in generative AI. In this research, we exploit a diffusion model capable of generating high-quality panoramic images. We generate images of interview rooms capable of delivering immersive interview experiences via refined text prompts. The resulting imagery is then reconstituted 3D VR content utilizing the Unity engine, facilitating enhanced interaction and engagement within virtual environments. This research compares and analyzes various methods presented in related research and proposes a new process for efficiently constructing 360-degree virtual environments. When wearing Oculus Quest 2 and experiencing the virtual environment created using the proposed method, a high sense of immersion was experienced, similar to the actual interview environment. © 2024 IEEE.
2023
Si, J.; Yang, S.; Kim, D.; Kim, S.
Metaverse Interview Room Creation With Virtual Interviewer Generation Using Diffusion Model Proceedings Article
In: Proc. IEEE Asia-Pacific Conf. Comput. Sci. Data Eng., CSDE, Institute of Electrical and Electronics Engineers Inc., 2023, ISBN: 979-835034107-2 (ISBN).
Abstract | Links | BibTeX | Tags: Changing trends, Cutting edges, Diffusion, Diffusion Model, Generative AI, Hiring process, Interview skills, It focus, Metaverse, Metaverses, Unity, Virtual Interview, Virtual Reality
@inproceedings{si_metaverse_2023,
title = {Metaverse Interview Room Creation With Virtual Interviewer Generation Using Diffusion Model},
author = {J. Si and S. Yang and D. Kim and S. Kim},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190586380&doi=10.1109%2fCSDE59766.2023.10487677&partnerID=40&md5=9ea374e1fef25598abf12d7636054d89},
doi = {10.1109/CSDE59766.2023.10487677},
isbn = {979-835034107-2 (ISBN)},
year = {2023},
date = {2023-01-01},
booktitle = {Proc. IEEE Asia-Pacific Conf. Comput. Sci. Data Eng., CSDE},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Virtual interviews are an effective way to respond quickly to the changing trends of our time and adapt flexibly to the hiring processes of various organizations. Through this method, applicants have the opportunity to practice their interview skills and receive feedback, greatly aiding their job preparation. Additionally, experiencing a virtual interview environment that is similar to an actual one enables them to adapt more easily to a variety of new interview situations. This paper delves deeply into the virtual interview environment implemented by combining cutting-edge metaverse technology and generative AI. Specifically, it focuses on creating an environment utilizing realistic Diffusion models to generate interviewers, enabling the provision of scenarios that are similar to actual interviews. © 2023 IEEE.},
keywords = {Changing trends, Cutting edges, Diffusion, Diffusion Model, Generative AI, Hiring process, Interview skills, It focus, Metaverse, Metaverses, Unity, Virtual Interview, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Virtual interviews are an effective way to respond quickly to the changing trends of our time and adapt flexibly to the hiring processes of various organizations. Through this method, applicants have the opportunity to practice their interview skills and receive feedback, greatly aiding their job preparation. Additionally, experiencing a virtual interview environment that is similar to an actual one enables them to adapt more easily to a variety of new interview situations. This paper delves deeply into the virtual interview environment implemented by combining cutting-edge metaverse technology and generative AI. Specifically, it focuses on creating an environment utilizing realistic Diffusion models to generate interviewers, enabling the provision of scenarios that are similar to actual interviews. © 2023 IEEE.
Si, J.; Song, J.; Woo, M.; Kim, D.; Lee, Y.; Kim, S.
Generative AI Models for Virtual Interviewers: Applicability and Performance Comparison Proceedings Article
In: IET. Conf. Proc., pp. 27–28, Institution of Engineering and Technology, 2023, ISBN: 27324494 (ISSN).
Abstract | Links | BibTeX | Tags: 3D Generation, College admissions, Digital elevation model, Effective practices, Generative AI, Job hunting, Metaverse, Metaverses, Performance, Performance comparison, Virtual environments, Virtual Interview, Virtual Reality
@inproceedings{si_generative_2023,
title = {Generative AI Models for Virtual Interviewers: Applicability and Performance Comparison},
author = {J. Si and J. Song and M. Woo and D. Kim and Y. Lee and S. Kim},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203492324&doi=10.1049%2ficp.2024.0193&partnerID=40&md5=84eb48f6b51c941da9c77fa3aba46262},
doi = {10.1049/icp.2024.0193},
isbn = {27324494 (ISSN)},
year = {2023},
date = {2023-01-01},
booktitle = {IET. Conf. Proc.},
volume = {2023},
pages = {27–28},
publisher = {Institution of Engineering and Technology},
abstract = {Interviewing processes are considered crucial steps in job hunting or college admissions, and effective practice plays a significant role in successfully navigating these stages. Although various platforms have recently emerged for practicing virtual interviews, they often lack the tension and realism of actual interviews due to repetitive and formal content. This study aims to analyze and compare the performance of different generative AI models for creating a diverse set of virtual interviewers. Specifically, we examine the characteristics and applicability of each model, as well as the differences and advantages between them, and evaluate the performance of the generated virtual interviewers. Through this analysis, we aim to propose solutions for enhancing the practicality and efficiency of virtual interviews. © The Institution of Engineering & Technology 2023.},
keywords = {3D Generation, College admissions, Digital elevation model, Effective practices, Generative AI, Job hunting, Metaverse, Metaverses, Performance, Performance comparison, Virtual environments, Virtual Interview, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Interviewing processes are considered crucial steps in job hunting or college admissions, and effective practice plays a significant role in successfully navigating these stages. Although various platforms have recently emerged for practicing virtual interviews, they often lack the tension and realism of actual interviews due to repetitive and formal content. This study aims to analyze and compare the performance of different generative AI models for creating a diverse set of virtual interviewers. Specifically, we examine the characteristics and applicability of each model, as well as the differences and advantages between them, and evaluate the performance of the generated virtual interviewers. Through this analysis, we aim to propose solutions for enhancing the practicality and efficiency of virtual interviews. © The Institution of Engineering & Technology 2023.