AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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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.
2025
Casas, L.; Hannah, S.; Mitchell, K.
HoloJig: Interactive Spoken Prompt Specified Generative AI Environments Journal Article
In: IEEE Computer Graphics and Applications, vol. 45, no. 2, pp. 69–77, 2025, ISSN: 02721716 (ISSN).
Abstract | Links | BibTeX | Tags: 3-D rendering, Article, Collaborative workspace, customer experience, Economic and social effects, generative artificial intelligence, human, Immersive, Immersive environment, parallax, Real- time, simulation, Simulation training, speech, Time based, Virtual environments, Virtual Reality, Virtual reality experiences, Virtual spaces, VR systems
@article{casas_holojig_2025,
title = {HoloJig: Interactive Spoken Prompt Specified Generative AI Environments},
author = {L. Casas and S. Hannah and K. Mitchell},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001182100&doi=10.1109%2fMCG.2025.3553780&partnerID=40&md5=ec5dc44023314b6f9221169357d81dcd},
doi = {10.1109/MCG.2025.3553780},
issn = {02721716 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Computer Graphics and Applications},
volume = {45},
number = {2},
pages = {69–77},
abstract = {HoloJig offers an interactive, speech-to-virtual reality (VR), VR experience that generates diverse environments in real time based on live spoken descriptions. Unlike traditional VR systems that rely on prebuilt assets, HoloJig dynamically creates personalized and immersive virtual spaces with depth-based parallax 3-D rendering, allowing users to define the characteristics of their immersive environment through verbal prompts. This generative approach opens up new possibilities for interactive experiences, including simulations, training, collaborative workspaces, and entertainment. In addition to speech-to-VR environment generation, a key innovation of HoloJig is its progressive visual transition mechanism, which smoothly dissolves between previously generated and newly requested environments, mitigating the delay caused by neural computations. This feature ensures a seamless and continuous user experience, even as new scenes are being rendered on remote servers. © 1981-2012 IEEE.},
keywords = {3-D rendering, Article, Collaborative workspace, customer experience, Economic and social effects, generative artificial intelligence, human, Immersive, Immersive environment, parallax, Real- time, simulation, Simulation training, speech, Time based, Virtual environments, Virtual Reality, Virtual reality experiences, Virtual spaces, VR systems},
pubstate = {published},
tppubtype = {article}
}
HoloJig offers an interactive, speech-to-virtual reality (VR), VR experience that generates diverse environments in real time based on live spoken descriptions. Unlike traditional VR systems that rely on prebuilt assets, HoloJig dynamically creates personalized and immersive virtual spaces with depth-based parallax 3-D rendering, allowing users to define the characteristics of their immersive environment through verbal prompts. This generative approach opens up new possibilities for interactive experiences, including simulations, training, collaborative workspaces, and entertainment. In addition to speech-to-VR environment generation, a key innovation of HoloJig is its progressive visual transition mechanism, which smoothly dissolves between previously generated and newly requested environments, mitigating the delay caused by neural computations. This feature ensures a seamless and continuous user experience, even as new scenes are being rendered on remote servers. © 1981-2012 IEEE.
Pielage, L.; Schmidle, P.; Marschall, B.; Risse, B.
Interactive High-Quality Skin Lesion Generation using Diffusion Models for VR-based Dermatological Education Proceedings Article
In: Int Conf Intell User Interfaces Proc IUI, pp. 878–897, Association for Computing Machinery, 2025, ISBN: 979-840071306-4 (ISBN).
Abstract | Links | BibTeX | Tags: Deep learning, Dermatology, Diffusion Model, diffusion models, Digital elevation model, Generative AI, Graphical user interfaces, Guidance Strategies, Guidance strategy, Image generation, Image generations, Inpainting, Interactive Generation, Medical education, Medical Imaging, Simulation training, Skin lesion, Upsampling, Virtual environments, Virtual Reality
@inproceedings{pielage_interactive_2025,
title = {Interactive High-Quality Skin Lesion Generation using Diffusion Models for VR-based Dermatological Education},
author = {L. Pielage and P. Schmidle and B. Marschall and B. Risse},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001923208&doi=10.1145%2f3708359.3712101&partnerID=40&md5=639eec55b08a54ce813f7c1016c621e7},
doi = {10.1145/3708359.3712101},
isbn = {979-840071306-4 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Int Conf Intell User Interfaces Proc IUI},
pages = {878–897},
publisher = {Association for Computing Machinery},
abstract = {Malignant melanoma is one of the most lethal forms of cancer when not detected early. As a result, cancer screening programs have been implemented internationally, all of which require visual inspection of skin lesions. Early melanoma detection is a crucial competence in medical and dermatological education, and it is primarily trained using 2D imagery. However, given the intrinsic 3D nature of skin lesions and the importance of incorporating additional contextual information about the patient (e.g., skin type, nearby lesions, etc.), this approach falls short of providing a comprehensive and scalable learning experience. A potential solution is the use of Virtual Reality (VR) scenarios, which can offer an effective strategy to train skin cancer screenings in a realistic 3D setting, thereby enhancing medical students' awareness of early melanoma detection. In this paper, we present a comprehensive pipeline and models for generating malignant melanomas and benign nevi, which can be utilized in VR-based medical training. We use diffusion models for the generation of skin lesions, which we have enhanced with various guiding strategies to give educators maximum flexibility in designing scenarios and seamlessly placing lesions on virtual agents. Additionally, we have developed a tool which comprises a graphical user interface (GUI) enabling the generation of new lesions and adapting existing ones using an intuitive and interactive inpainting strategy. The tool also offers a novel custom upsampling strategy to achieve a sufficient resolution required for diagnostic purposes. The generated skin lesions have been validated in a user study with trained dermatologists, confirming the overall high quality of the generated lesions and the utility for educational purposes. © 2025 Copyright held by the owner/author(s).},
keywords = {Deep learning, Dermatology, Diffusion Model, diffusion models, Digital elevation model, Generative AI, Graphical user interfaces, Guidance Strategies, Guidance strategy, Image generation, Image generations, Inpainting, Interactive Generation, Medical education, Medical Imaging, Simulation training, Skin lesion, Upsampling, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Malignant melanoma is one of the most lethal forms of cancer when not detected early. As a result, cancer screening programs have been implemented internationally, all of which require visual inspection of skin lesions. Early melanoma detection is a crucial competence in medical and dermatological education, and it is primarily trained using 2D imagery. However, given the intrinsic 3D nature of skin lesions and the importance of incorporating additional contextual information about the patient (e.g., skin type, nearby lesions, etc.), this approach falls short of providing a comprehensive and scalable learning experience. A potential solution is the use of Virtual Reality (VR) scenarios, which can offer an effective strategy to train skin cancer screenings in a realistic 3D setting, thereby enhancing medical students' awareness of early melanoma detection. In this paper, we present a comprehensive pipeline and models for generating malignant melanomas and benign nevi, which can be utilized in VR-based medical training. We use diffusion models for the generation of skin lesions, which we have enhanced with various guiding strategies to give educators maximum flexibility in designing scenarios and seamlessly placing lesions on virtual agents. Additionally, we have developed a tool which comprises a graphical user interface (GUI) enabling the generation of new lesions and adapting existing ones using an intuitive and interactive inpainting strategy. The tool also offers a novel custom upsampling strategy to achieve a sufficient resolution required for diagnostic purposes. The generated skin lesions have been validated in a user study with trained dermatologists, confirming the overall high quality of the generated lesions and the utility for educational purposes. © 2025 Copyright held by the owner/author(s).