Publications

@inproceedings{otsuka_development_2025,

title = {Development of A Relaxation Support System Utilizing Stereophonic AR},

author = {T. Otsuka and D. Li and P. Siriaraya and S. Nakajima},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105006602014&doi=10.1109%2fICNC64010.2025.10993739&partnerID=40&md5=abdaca1aefc88381072c1e8090697638},

doi = {10.1109/ICNC64010.2025.10993739},

isbn = {979-833152096-0 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Int. Conf. Comput., Netw. Commun., ICNC},

pages = {463–467},

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

abstract = {Given the high prevalence of stress and anxiety in today's society, there is an urgent need to explore effective methods to help people manage stress. This research aims to develop a relaxation support system using stereophonic augmented reality (AR), designed to help alleviate stress by recreating relaxing environments with immersive stereo soundscapes, including stories created from generative AI and environmental sounds while users are going for a walk. This paper presents a preliminary evaluation of the effectiveness of the proposed relaxation support method. © 2025 IEEE.},

keywords = {Augmented Reality, Environmental sounds, Generative AI, Immersive, Mental Well-being, Soundscapes, Spatial Audio, Stereo image processing, Support method, Support systems, Well being},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jayaraman_texavi_2024,

title = {TexAVi: Generating Stereoscopic VR Video Clips from Text Descriptions},

author = {S. Jayaraman and R. Bhavya and V. Srihari and V. Mary Anita Rajam},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215265234&doi=10.1109%2fCVMI61877.2024.10782691&partnerID=40&md5=8e20576af67b917ecfad83873a87ef29},

doi = {10.1109/CVMI61877.2024.10782691},

isbn = {979-835037687-6 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {IEEE Int. Conf. Comput. Vis. Mach. Intell., CVMI},

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

abstract = {While generative models such as text-to-image, large language models and text-to-video have seen significant progress, the extension to text-to-virtual-reality remains largely unexplored, due to a deficit in training data and the complexity of achieving realistic depth and motion in virtual environments. This paper proposes an approach to coalesce existing generative systems to form a stereoscopic virtual reality video from text. Carried out in three main stages, we start with a base text-to-image model that captures context from an input text. We then employ Stable Diffusion on the rudimentary image produced, to generate frames with enhanced realism and overall quality. These frames are processed with depth estimation algorithms to create left-eye and right-eye views, which are stitched side-by-side to create an immersive viewing experience. Such systems would be highly beneficial in virtual reality production, since filming and scene building often require extensive hours of work and post-production effort. We utilize image evaluation techniques, specifically Fréchet Inception Distance and CLIP Score, to assess the visual quality of frames produced for the video. These quantitative measures establish the proficiency of the proposed method. Our work highlights the exciting possibilities of using natural language-driven graphics in fields like virtual reality simulations. © 2024 IEEE.},

keywords = {Adversarial networks, Computer simulation languages, Deep learning, Depth Estimation, Depth perception, Diffusion Model, diffusion models, Digital elevation model, Generative adversarial networks, Generative model, Generative systems, Language Model, Motion capture, Stereo image processing, Text-to-image, Training data, Video analysis, Video-clips, Virtual environments, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}