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{he_enhancing_2024,

title = {Enhancing Narratives with SayMotion's text-to-3D animation and LLMs},

author = {K. He and A. Lapham and Z. Li},

editor = {Spencer S.N.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200655076&doi=10.1145%2f3641520.3665309&partnerID=40&md5=458f935043e3372e633ed5fc13bf6cd7},

doi = {10.1145/3641520.3665309},

isbn = {979-840070526-7 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. - SIGGRAPH Real-Time Live!},

publisher = {Association for Computing Machinery, Inc},

abstract = {SayMotion, a generative AI text-to-3D animation platform, utilizes deep generative learning and advanced physics simulation to transform text descriptions into realistic 3D human motions for applications in gaming, extended reality (XR), film production, education and interactive media. SayMotion addresses challenges due to the complexities of animation creation by employing a Large Language Model (LLM) fine-tuned to human motion with further AI-based animation editing components including spatial-temporal Inpainting via a proprietary Large Motion Model (LMM). SayMotion is a pioneer in the animation market by offering a comprehensive set of AI generation and AI editing functions for creating 3D animations efficiently and intuitively. With an LMM at its core, SayMotion aims to democratize 3D animations for everyone through language and generative motion. © 2024 Owner/Author.},

keywords = {3D animation, AI-based animation, Animation, Animation editing, Deep learning, Film production, Human motions, Interactive computer graphics, Interactive media, Language Model, Motion models, Physics simulation, Production medium, Simulation platform, Three dimensional computer graphics},

pubstate = {published},

tppubtype = {inproceedings}

}