Publications

@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{martini_semi_2024,

title = {Semi -Automated Digital Human Production for Enhanced Media Broadcasting},

author = {M. Martini and V. Valentini and A. Ciprian and A. Bottino and R. Iacoviello and M. Montagnuolo and A. Messina and F. Strada and D. Zappia},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199536742&doi=10.1109%2fGEM61861.2024.10585601&partnerID=40&md5=3703fba931b02f9615316db8ebbca70c},

doi = {10.1109/GEM61861.2024.10585601},

isbn = {979-835037453-7 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {IEEE Gaming, Entertain., Media Conf., GEM},

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

abstract = {In recent years, the application of synthetic humans in various fields has attracted considerable attention, leading to extensive exploration of their integration into the Metaverse and virtual production environments. This work presents a semi-automated approach that aims to find a fair trade-off between high-quality outputs and efficient production times. The project focuses on the Rai photo and video archives to find images of target characters for texturing and 3D reconstruction with the goal of reviving Rai's 2D footage and enhance the media experience. A key aspect of this study is to minimize the human intervention, ensuring an efficient, flexible, and scalable creation process. In this work, the improvements have been distributed among different stages of the digital human creation process, starting with the generation of 3D head meshes from 2D images of the reference character and then moving on to the generation, using a Diffusion model, of suitable images for texture development. These assets are then integrated into the Unreal Engine, where a custom widget facilitates posing, rendering, and texturing of Synthetic Humans models. Finally, an in-depth quantitative comparison and subjective tests were carried out between the original character images and the rendered synthetic humans, confirming the validity of the approach. © 2024 IEEE.},

keywords = {AI automation, Automation, Creation process, Digital humans, Economic and social effects, Extensive explorations, Face reconstruction, Generative AI, Image enhancement, media archive, Media archives, Metaverses, Rendering (computer graphics), Synthetic human, Synthetic Humans, Textures, Three dimensional computer graphics, Virtual production, Virtual Reality},

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}

}