Publications

@inproceedings{rasch_creepycocreator_2025,

title = {CreepyCoCreator? Investigating AI Representation Modes for 3D Object Co-Creation in Virtual Reality},

author = {J. Rasch and J. Töws and T. Hirzle and F. Müller and M. Schmitz},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005742763&doi=10.1145%2f3706598.3713720&partnerID=40&md5=e6cdcb6cc7249a8836ecc39ae103cd53},

doi = {10.1145/3706598.3713720},

isbn = {979-840071394-1 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Conf Hum Fact Comput Syst Proc},

publisher = {Association for Computing Machinery},

abstract = {Generative AI in Virtual Reality offers the potential for collaborative object-building, yet challenges remain in aligning AI contributions with user expectations. In particular, users often struggle to understand and collaborate with AI when its actions are not transparently represented. This paper thus explores the co-creative object-building process through a Wizard-of-Oz study, focusing on how AI can effectively convey its intent to users during object customization in Virtual Reality. Inspired by human-to-human collaboration, we focus on three representation modes: the presence of an embodied avatar, whether the AI's contributions are visualized immediately or incrementally, and whether the areas modified are highlighted in advance. The findings provide insights into how these factors affect user perception and interaction with object-generating AI tools in Virtual Reality as well as satisfaction and ownership of the created objects. The results offer design implications for co-creative world-building systems, aiming to foster more effective and satisfying collaborations between humans and AI in Virtual Reality. © 2025 Copyright held by the owner/author(s).},

keywords = {3D Creation, 3D modeling, 3D object, Building process, Co-creation, Co-creative system, Co-creative systems, Creative systems, Creatives, Generative AI, Three dimensional computer graphics, User expectations, User Studies, User study, Virtual Reality, Virtualization},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@inproceedings{zhang_fusionprotor_2025,

title = {FusionProtor: A Mixed-Prototype Tool for Component-level Physical-to-Virtual 3D Transition and Simulation},

author = {H. Zhang and P. Chen and X. Xie and Z. Jiang and Y. Wu and Z. Li and X. Chen and L. Sun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005745450&doi=10.1145%2f3706598.3713686&partnerID=40&md5=e51eac0cc99293538422d98a4070cd09},

doi = {10.1145/3706598.3713686},

isbn = {979-840071394-1 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Conf Hum Fact Comput Syst Proc},

publisher = {Association for Computing Machinery},

abstract = {Developing and simulating 3D prototypes is crucial in product conceptual design for ideation and presentation. Traditional methods often keep physical and virtual prototypes separate, leading to a disjointed prototype workflow. In addition, acquiring high-fidelity prototypes is time-consuming and resource-intensive, distracting designers from creative exploration. Recent advancements in generative artificial intelligence (GAI) and extended reality (XR) provided new solutions for rapid prototype transition and mixed simulation. We conducted a formative study to understand current challenges in the traditional prototype process and explore how to effectively utilize GAI and XR ability in prototype. Then we introduced FusionProtor, a mixed-prototype tool for component-level 3D prototype transition and simulation. We proposed a step-by-step generation pipeline in FusionProtor, effectively transiting 3D prototypes from physical to virtual and low- to high-fidelity for rapid ideation and iteration. We also innovated a component-level 3D creation method and applied it in XR environment for the mixed-prototype presentation and interaction. We conducted technical and user experiments to verify FusionProtor's usability in supporting diverse designs. Our results verified that it achieved a seamless workflow between physical and virtual domains, enhancing efficiency and promoting ideation. We also explored the effect of mixed interaction on design and critically discussed its best practices for HCI community. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.},

keywords = {3D modeling, 3D prototype, 3D simulations, 3d transition, Component levels, Conceptual design, Creatives, Generative AI, High-fidelity, Integrated circuit layout, Mixed reality, Product conceptual designs, Prototype tools, Prototype workflow, Three dimensional computer graphics, Usability engineering, Virtual Prototyping},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{vallasciani_creaixr_2024,

title = {CreAIXR: Fostering Creativity with Generative AI in XR environments},

author = {G. Vallasciani and L. Stacchio and P. Cascarano and G. Marfia},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211481990&doi=10.1109%2fMetaCom62920.2024.00034&partnerID=40&md5=002e25a2d4ddb170e21029b27c157b28},

doi = {10.1109/MetaCom62920.2024.00034},

isbn = {979-833151599-7 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. - IEEE Int. Conf. Metaverse Comput., Netw., Appl., MetaCom},

pages = {1–8},

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

abstract = {Fostering creativity is paramount for cultivating innovative minds capable of addressing complex challenges. Modern technologies like eXtended Reality (XR) and Artificial Intelligence (AI) may nurture grounds supporting creative thinking by providing immersive and manipulable environments. An open research question is how such technologies may best lead to such a possible result. To help move one step closer to an answer, we present a portable XR platform, namely CreAIXR, where objects may be creatively defined and manipulated with AI paradigms. CreAIXR leverages web technologies, XR, and generative AI where creatives are immersed in a composable experience, allowing them to collaborate and customize an immersive environment through XR paradigms and generative AI. We here describe this system along with its validation through experiments carried out with a group of individuals having a background in the field of visual arts. © 2024 IEEE.},

keywords = {Artificial intelligence, Creative thinking, Creatives, Creativity, Extended reality, Generative adversarial networks, generative artificial intelligence, Immersive, Modern technologies, Research questions, Stable Diffusion, Web technologies},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jacoby_efficient_2023,

title = {Efficient Cloud Pipelines for Neural Radiance Fields},

author = {D. Jacoby and D. Xu and W. Ribas and M. Xu and T. Liu and V. Jeyaraman and M. Wei and E. D. Blois and Y. Coady},

editor = {Chakrabarti S. and Paul R.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179765347&doi=10.1109%2fUEMCON59035.2023.10316126&partnerID=40&md5=2640a2b033c9200560f93898a178dbbe},

doi = {10.1109/UEMCON59035.2023.10316126},

isbn = {979-835030413-8 (ISBN)},

year  = {2023},

date = {2023-01-01},

booktitle = {IEEE Annu. Ubiquitous Comput., Electron. Mob. Commun. Conf., UEMCON},

pages = {114–119},

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

abstract = {Since their introduction in 2020, Neural Radiance Fields (NeRFs) have taken the computer vision community by storm. They provide a multi-view representation of a scene or object that is ideal for eXtended Reality (XR) applications and for creative endeavors such as virtual production, as well as change detection operations in geospatial analytics. The computational cost of these generative AI models is quite high, however, and the construction of cloud pipelines to generate NeRFs is neccesary to realize their potential in client applications. In this paper, we present pipelines on a high performance academic computing cluster and compare it with a pipeline implemented on Microsoft Azure. Along the way, we describe some uses of NeRFs in enabling novel user interaction scenarios. © 2023 IEEE.},

keywords = {Azure, Change detection, Cloud analytics, Cloud computing, Cloud-computing, Cluster computing, Containerization, Creatives, Geo-spatial, Multi-views, Neural radiance field, Neural Radiance Fields, Pipelines, User interfaces, Virtual production, Vision communities, Windows operating system},

pubstate = {published},

tppubtype = {inproceedings}

}