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 expand the Abstract, Links and BibTex record for each paper.
2025
Peter, K.; Makosa, I.; Auala, S.; Ndjao, L.; Maasz, D.; Mbinge, U.; Winschiers-Theophilus, H.
Co-creating a VR Narrative Experience of Constructing a Food Storage Following OvaHimba Traditional Practices Proceedings Article
In: IMX - Proc. ACM Int. Conf. Interact. Media Experiences, pp. 418–423, Association for Computing Machinery, Inc, 2025, ISBN: 979-840071391-0 (ISBN).
Abstract | Links | BibTeX | Tags: 3D Modelling, 3D models, 3d-modeling, Co-designs, Community-based, Community-Based Co-Design, Computer aided design, Cultural heritage, Cultural heritages, Food storage, Human computer interaction, Human engineering, Indigenous Knowledge, Information Systems, Interactive computer graphics, Interactive computer systems, IVR, Namibia, OvaHimba, Ovahimbum, Photogrammetry, Sustainable development, Virtual environments, Virtual Reality
@inproceedings{peter_co-creating_2025,
title = {Co-creating a VR Narrative Experience of Constructing a Food Storage Following OvaHimba Traditional Practices},
author = {K. Peter and I. Makosa and S. Auala and L. Ndjao and D. Maasz and U. Mbinge and H. Winschiers-Theophilus},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007984089&doi=10.1145%2f3706370.3731652&partnerID=40&md5=36f95823413852d636b39bd561c97917},
doi = {10.1145/3706370.3731652},
isbn = {979-840071391-0 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {IMX - Proc. ACM Int. Conf. Interact. Media Experiences},
pages = {418–423},
publisher = {Association for Computing Machinery, Inc},
abstract = {As part of an attempt to co-create a comprehensive virtual environment in which one can explore and learn traditional practices of the OvaHimba people, we have co-designed and implemented a VR experience to construct a traditional food storage. In collaboration with the OvaHimba community residing in Otjisa, we have explored culturally valid representations of the process. We have further investigated different techniques such as photogrammetry, generative AI and manual methods to develop 3D models. Our findings highlight the importance of context, process, and community-defined relevance in co-design, the fluidity of cultural realities and virtual representations, as well as technical challenges. © 2025 Copyright held by the owner/author(s).},
keywords = {3D Modelling, 3D models, 3d-modeling, Co-designs, Community-based, Community-Based Co-Design, Computer aided design, Cultural heritage, Cultural heritages, Food storage, Human computer interaction, Human engineering, Indigenous Knowledge, Information Systems, Interactive computer graphics, Interactive computer systems, IVR, Namibia, OvaHimba, Ovahimbum, Photogrammetry, Sustainable development, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
As part of an attempt to co-create a comprehensive virtual environment in which one can explore and learn traditional practices of the OvaHimba people, we have co-designed and implemented a VR experience to construct a traditional food storage. In collaboration with the OvaHimba community residing in Otjisa, we have explored culturally valid representations of the process. We have further investigated different techniques such as photogrammetry, generative AI and manual methods to develop 3D models. Our findings highlight the importance of context, process, and community-defined relevance in co-design, the fluidity of cultural realities and virtual representations, as well as technical challenges. © 2025 Copyright held by the owner/author(s).
Coronado, A.; Carvalho, S. T.; Berretta, L.
See Through My Eyes: Using Multimodal Large Language Model for Describing Rendered Environments to Blind People Proceedings Article
In: IMX - Proc. ACM Int. Conf. Interact. Media Experiences, pp. 451–457, Association for Computing Machinery, Inc, 2025, ISBN: 979-840071391-0 (ISBN).
Abstract | Links | BibTeX | Tags: Accessibility, Behavioral Research, Blind, Blind people, Helmet mounted displays, Human engineering, Human rehabilitation equipment, Interactive computer graphics, Interactive computer systems, Language Model, LLM, Multi-modal, Rendered environment, rendered environments, Spatial cognition, Virtual Reality, Vision aids, Visual impairment, Visual languages, Visually impaired people
@inproceedings{coronado_see_2025,
title = {See Through My Eyes: Using Multimodal Large Language Model for Describing Rendered Environments to Blind People},
author = {A. Coronado and S. T. Carvalho and L. Berretta},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007991842&doi=10.1145%2f3706370.3731641&partnerID=40&md5=2f7cb1535d39d5e59b1f43f773de3272},
doi = {10.1145/3706370.3731641},
isbn = {979-840071391-0 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {IMX - Proc. ACM Int. Conf. Interact. Media Experiences},
pages = {451–457},
publisher = {Association for Computing Machinery, Inc},
abstract = {Extended Reality (XR) is quickly expanding "as the next major technology wave in personal computing". Nevertheless, this expansion and adoption could also exclude certain disabled users, particularly people with visual impairment (VIP). According to the World Health Organization (WHO) in their 2019 publication, there were at least 2.2 billion people with visual impairment, a number that is also estimated to have increased in recent years. Therefore, it is important to include disabled users, especially visually impaired people, in the design of Head-Mounted Displays and Extended Reality environments. Indeed, this objective can be pursued by incorporating Multimodal Large Language Model (MLLM) technology, which can assist visually impaired people. As a case study, this study employs different prompts that result in environment descriptions from an MLLM integrated into a virtual reality (VR) escape room. Therefore, six potential prompts were engineered to generate valuable outputs for visually impaired users inside a VR environment. These outputs were evaluated using the G-Eval, and VIEScore metrics. Even though, the results show that the prompt patterns provided a description that aligns with the user's point of view, it is highly recommended to evaluate these outputs through "expected outputs"from Orientation and Mobility Specialists, and Sighted Guides. Furthermore, the subsequent step in the process is to evaluate these outputs by visually impaired people themselves to identify the most effective prompt pattern. © 2025 Copyright held by the owner/author(s).},
keywords = {Accessibility, Behavioral Research, Blind, Blind people, Helmet mounted displays, Human engineering, Human rehabilitation equipment, Interactive computer graphics, Interactive computer systems, Language Model, LLM, Multi-modal, Rendered environment, rendered environments, Spatial cognition, Virtual Reality, Vision aids, Visual impairment, Visual languages, Visually impaired people},
pubstate = {published},
tppubtype = {inproceedings}
}
Extended Reality (XR) is quickly expanding "as the next major technology wave in personal computing". Nevertheless, this expansion and adoption could also exclude certain disabled users, particularly people with visual impairment (VIP). According to the World Health Organization (WHO) in their 2019 publication, there were at least 2.2 billion people with visual impairment, a number that is also estimated to have increased in recent years. Therefore, it is important to include disabled users, especially visually impaired people, in the design of Head-Mounted Displays and Extended Reality environments. Indeed, this objective can be pursued by incorporating Multimodal Large Language Model (MLLM) technology, which can assist visually impaired people. As a case study, this study employs different prompts that result in environment descriptions from an MLLM integrated into a virtual reality (VR) escape room. Therefore, six potential prompts were engineered to generate valuable outputs for visually impaired users inside a VR environment. These outputs were evaluated using the G-Eval, and VIEScore metrics. Even though, the results show that the prompt patterns provided a description that aligns with the user's point of view, it is highly recommended to evaluate these outputs through "expected outputs"from Orientation and Mobility Specialists, and Sighted Guides. Furthermore, the subsequent step in the process is to evaluate these outputs by visually impaired people themselves to identify the most effective prompt pattern. © 2025 Copyright held by the owner/author(s).
Behravan, M.; Gračanin, D.
From Voices to Worlds: Developing an AI-Powered Framework for 3D Object Generation in Augmented Reality Proceedings Article
In: Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW, pp. 150–155, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833151484-6 (ISBN).
Abstract | Links | BibTeX | Tags: 3D modeling, 3D object, 3D Object Generation, 3D reconstruction, Augmented Reality, Cutting edges, Generative AI, Interactive computer systems, Language Model, Large language model, large language models, matrix, Multilingual speech interaction, Real- time, Speech enhancement, Speech interaction, Volume Rendering
@inproceedings{behravan_voices_2025,
title = {From Voices to Worlds: Developing an AI-Powered Framework for 3D Object Generation in Augmented Reality},
author = {M. Behravan and D. Gračanin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005153589&doi=10.1109%2fVRW66409.2025.00038&partnerID=40&md5=b8aaab4e2378cde3595d98d79266d371},
doi = {10.1109/VRW66409.2025.00038},
isbn = {979-833151484-6 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW},
pages = {150–155},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {This paper presents Matrix, an advanced AI-powered framework designed for real-time 3D object generation in Augmented Reality (AR) environments. By integrating a cutting-edge text-to-3D generative AI model, multilingual speech-to-text translation, and large language models (LLMs), the system enables seamless user interactions through spoken commands. The framework processes speech inputs, generates 3D objects, and provides object recommendations based on contextual understanding, enhancing AR experiences. A key feature of this framework is its ability to optimize 3D models by reducing mesh complexity, resulting in significantly smaller file sizes and faster processing on resource-constrained AR devices. Our approach addresses the challenges of high GPU usage, large model output sizes, and real-time system responsiveness, ensuring a smoother user experience. Moreover, the system is equipped with a pre-generated object repository, further reducing GPU load and improving efficiency. We demonstrate the practical applications of this framework in various fields such as education, design, and accessibility, and discuss future enhancements including image-to-3D conversion, environmental object detection, and multimodal support. The open-source nature of the framework promotes ongoing innovation and its utility across diverse industries. © 2025 IEEE.},
keywords = {3D modeling, 3D object, 3D Object Generation, 3D reconstruction, Augmented Reality, Cutting edges, Generative AI, Interactive computer systems, Language Model, Large language model, large language models, matrix, Multilingual speech interaction, Real- time, Speech enhancement, Speech interaction, Volume Rendering},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents Matrix, an advanced AI-powered framework designed for real-time 3D object generation in Augmented Reality (AR) environments. By integrating a cutting-edge text-to-3D generative AI model, multilingual speech-to-text translation, and large language models (LLMs), the system enables seamless user interactions through spoken commands. The framework processes speech inputs, generates 3D objects, and provides object recommendations based on contextual understanding, enhancing AR experiences. A key feature of this framework is its ability to optimize 3D models by reducing mesh complexity, resulting in significantly smaller file sizes and faster processing on resource-constrained AR devices. Our approach addresses the challenges of high GPU usage, large model output sizes, and real-time system responsiveness, ensuring a smoother user experience. Moreover, the system is equipped with a pre-generated object repository, further reducing GPU load and improving efficiency. We demonstrate the practical applications of this framework in various fields such as education, design, and accessibility, and discuss future enhancements including image-to-3D conversion, environmental object detection, and multimodal support. The open-source nature of the framework promotes ongoing innovation and its utility across diverse industries. © 2025 IEEE.
Leininger, P.; Weber, C. J.; Rothe, S.
Understanding Creative Potential and Use Cases of AI-Generated Environments for Virtual Film Productions: Insights from Industry Professionals Proceedings Article
In: IMX - Proc. ACM Int. Conf. Interact. Media Experiences, pp. 60–78, Association for Computing Machinery, Inc, 2025, ISBN: 979-840071391-0 (ISBN).
Abstract | Links | BibTeX | Tags: 3-D environments, 3D reconstruction, 3D Scene Reconstruction, 3d scenes reconstruction, AI-generated 3d environment, AI-Generated 3D Environments, Computer interaction, Creative Collaboration, Creatives, Digital content creation, Digital Content Creation., Filmmaking workflow, Filmmaking Workflows, Gaussian distribution, Gaussian Splatting, Gaussians, Generative AI, Graphical user interface, Graphical User Interface (GUI), Graphical user interfaces, Human computer interaction, human-computer interaction, Human-Computer Interaction (HCI), Immersive, Immersive Storytelling, Interactive computer graphics, Interactive computer systems, Interactive media, Mesh generation, Previsualization, Real-Time Rendering, Splatting, Three dimensional computer graphics, Virtual production, Virtual Production (VP), Virtual Reality, Work-flows
@inproceedings{leininger_understanding_2025,
title = {Understanding Creative Potential and Use Cases of AI-Generated Environments for Virtual Film Productions: Insights from Industry Professionals},
author = {P. Leininger and C. J. Weber and S. Rothe},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007976841&doi=10.1145%2f3706370.3727853&partnerID=40&md5=0d4cf7a2398d12d04e4f0ab182474a10},
doi = {10.1145/3706370.3727853},
isbn = {979-840071391-0 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {IMX - Proc. ACM Int. Conf. Interact. Media Experiences},
pages = {60–78},
publisher = {Association for Computing Machinery, Inc},
abstract = {Virtual production (VP) is transforming filmmaking by integrating real-time digital elements with live-action footage, offering new creative possibilities and streamlined workflows. While industry experts recognize AI's potential to revolutionize VP, its practical applications and value across different production phases and user groups remain underexplored. Building on initial research into generative and data-driven approaches, this paper presents the first systematic pilot study evaluating three types of AI-generated 3D environments - Depth Mesh, 360° Panoramic Meshes, and Gaussian Splatting - through the participation of 15 filmmaking professionals from diverse roles. Unlike commonly used 2D AI-generated visuals, our approach introduces navigable 3D environments that offer greater control and flexibility, aligning more closely with established VP workflows. Through expert interviews and literature research, we developed evaluation criteria to assess their usefulness beyond concept development, extending to previsualization, scene exploration, and interdisciplinary collaboration. Our findings indicate that different environments cater to distinct production needs, from early ideation to detailed visualization. Gaussian Splatting proved effective for high-fidelity previsualization, while 360° Panoramic Meshes excelled in rapid concept ideation. Despite their promise, challenges such as limited interactivity and customization highlight areas for improvement. Our prototype, EnVisualAIzer, built in Unreal Engine 5, provides an accessible platform for diverse filmmakers to engage with AI-generated environments, fostering a more inclusive production process. By lowering technical barriers, these environments have the potential to make advanced VP tools more widely available. This study offers valuable insights into the evolving role of AI in VP and sets the stage for future research and development. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.},
keywords = {3-D environments, 3D reconstruction, 3D Scene Reconstruction, 3d scenes reconstruction, AI-generated 3d environment, AI-Generated 3D Environments, Computer interaction, Creative Collaboration, Creatives, Digital content creation, Digital Content Creation., Filmmaking workflow, Filmmaking Workflows, Gaussian distribution, Gaussian Splatting, Gaussians, Generative AI, Graphical user interface, Graphical User Interface (GUI), Graphical user interfaces, Human computer interaction, human-computer interaction, Human-Computer Interaction (HCI), Immersive, Immersive Storytelling, Interactive computer graphics, Interactive computer systems, Interactive media, Mesh generation, Previsualization, Real-Time Rendering, Splatting, Three dimensional computer graphics, Virtual production, Virtual Production (VP), Virtual Reality, Work-flows},
pubstate = {published},
tppubtype = {inproceedings}
}
Virtual production (VP) is transforming filmmaking by integrating real-time digital elements with live-action footage, offering new creative possibilities and streamlined workflows. While industry experts recognize AI's potential to revolutionize VP, its practical applications and value across different production phases and user groups remain underexplored. Building on initial research into generative and data-driven approaches, this paper presents the first systematic pilot study evaluating three types of AI-generated 3D environments - Depth Mesh, 360° Panoramic Meshes, and Gaussian Splatting - through the participation of 15 filmmaking professionals from diverse roles. Unlike commonly used 2D AI-generated visuals, our approach introduces navigable 3D environments that offer greater control and flexibility, aligning more closely with established VP workflows. Through expert interviews and literature research, we developed evaluation criteria to assess their usefulness beyond concept development, extending to previsualization, scene exploration, and interdisciplinary collaboration. Our findings indicate that different environments cater to distinct production needs, from early ideation to detailed visualization. Gaussian Splatting proved effective for high-fidelity previsualization, while 360° Panoramic Meshes excelled in rapid concept ideation. Despite their promise, challenges such as limited interactivity and customization highlight areas for improvement. Our prototype, EnVisualAIzer, built in Unreal Engine 5, provides an accessible platform for diverse filmmakers to engage with AI-generated environments, fostering a more inclusive production process. By lowering technical barriers, these environments have the potential to make advanced VP tools more widely available. This study offers valuable insights into the evolving role of AI in VP and sets the stage for future research and development. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Shawash, J.; Thibault, M.; Hamari, J.
Who Killed Helene Pumpulivaara?: AI-Assisted Content Creation and XR Implementation for Interactive Built Heritage Storytelling Proceedings Article
In: IMX - Proc. ACM Int. Conf. Interact. Media Experiences, pp. 377–379, Association for Computing Machinery, Inc, 2025, ISBN: 979-840071391-0 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Augmented Reality, Built heritage, Content creation, Digital heritage, Digital Interpretation, Extended reality, Human computer interaction, Human engineering, Industrial Heritage, Interactive computer graphics, Interactive computer systems, Mobile photographies, Narrative Design, Narrative designs, Production pipelines, Uncanny valley, Virtual Reality
@inproceedings{shawash_who_2025,
title = {Who Killed Helene Pumpulivaara?: AI-Assisted Content Creation and XR Implementation for Interactive Built Heritage Storytelling},
author = {J. Shawash and M. Thibault and J. Hamari},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105008003446&doi=10.1145%2f3706370.3731703&partnerID=40&md5=bc8a8d221abcf6c560446979fbd06cbc},
doi = {10.1145/3706370.3731703},
isbn = {979-840071391-0 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {IMX - Proc. ACM Int. Conf. Interact. Media Experiences},
pages = {377–379},
publisher = {Association for Computing Machinery, Inc},
abstract = {This demo presents "Who Killed Helene Pumpulivaara?", an innovative interactive heritage experience that combines crime mystery narrative with XR technology to address key challenges in digital heritage interpretation. Our work makes six significant contributions: (1) the discovery of a "Historical Uncanny Valley"effect where varying fidelity levels between AI-generated and authentic content serve as implicit markers distinguishing fact from interpretation; (2) an accessible production pipeline combining mobile photography with AI tools that democratizes XR heritage creation for resource-limited institutions; (3) a spatial storytelling approach that effectively counters decontextualization in digital heritage; (4) a multi-platform implementation strategy across web and VR environments; (5) a practical model for AI-assisted heritage content creation balancing authenticity with engagement; and (6) a pathway toward spatial augmented reality for future heritage interpretation. Using the historic Finlayson Factory in Tampere, Finland as a case study, our implementation demonstrates how emerging technologies can enrich the authenticity of heritage experiences, fostering deeper emotional connections between visitors and the histories embedded in place. © 2025 Copyright held by the owner/author(s).},
keywords = {Artificial intelligence, Augmented Reality, Built heritage, Content creation, Digital heritage, Digital Interpretation, Extended reality, Human computer interaction, Human engineering, Industrial Heritage, Interactive computer graphics, Interactive computer systems, Mobile photographies, Narrative Design, Narrative designs, Production pipelines, Uncanny valley, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
This demo presents "Who Killed Helene Pumpulivaara?", an innovative interactive heritage experience that combines crime mystery narrative with XR technology to address key challenges in digital heritage interpretation. Our work makes six significant contributions: (1) the discovery of a "Historical Uncanny Valley"effect where varying fidelity levels between AI-generated and authentic content serve as implicit markers distinguishing fact from interpretation; (2) an accessible production pipeline combining mobile photography with AI tools that democratizes XR heritage creation for resource-limited institutions; (3) a spatial storytelling approach that effectively counters decontextualization in digital heritage; (4) a multi-platform implementation strategy across web and VR environments; (5) a practical model for AI-assisted heritage content creation balancing authenticity with engagement; and (6) a pathway toward spatial augmented reality for future heritage interpretation. Using the historic Finlayson Factory in Tampere, Finland as a case study, our implementation demonstrates how emerging technologies can enrich the authenticity of heritage experiences, fostering deeper emotional connections between visitors and the histories embedded in place. © 2025 Copyright held by the owner/author(s).