AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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2025
Shen, Y.; Li, B.; Huang, J.; Wang, Z.
GaussianShopVR: Facilitating Immersive 3D Authoring Using Gaussian Splatting in VR Proceedings Article
In: Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW, pp. 1292–1293, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833151484-6 (ISBN).
Abstract | Links | BibTeX | Tags: 3D authoring, 3D modeling, Digital replicas, Gaussian distribution, Gaussian Splatting editing, Gaussians, Graphical user interfaces, High quality, Immersive, Immersive environment, Interactive computer graphics, Rendering (computer graphics), Rendering pipelines, Splatting, Three dimensional computer graphics, User profile, Virtual Reality, Virtual reality user interface, Virtualization, VR user interface
@inproceedings{shen_gaussianshopvr_2025,
title = {GaussianShopVR: Facilitating Immersive 3D Authoring Using Gaussian Splatting in VR},
author = {Y. Shen and B. Li and J. Huang and Z. Wang},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005138672&doi=10.1109%2fVRW66409.2025.00292&partnerID=40&md5=9b644bd19394a289d3027ab9a2dfed6a},
doi = {10.1109/VRW66409.2025.00292},
isbn = {979-833151484-6 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW},
pages = {1292–1293},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Virtual reality (VR) applications require massive high-quality 3D assets to create immersive environments. Generating mesh-based 3D assets typically involves a significant amount of manpower and effort, which makes VR applications less accessible. 3D Gaussian Splatting (3DGS) has attracted much attention for its ability to quickly create digital replicas of real-life scenes and its compatibility with traditional rendering pipelines. However, it remains a challenge to edit 3DGS in a flexible and controllable manner. We propose GaussianShopVR, a system that leverages VR user interfaces to specify target areas to achieve flexible and controllable editing of reconstructed 3DGS. In addition, selected areas can provide 3D information to generative AI models to facilitate the editing. GaussianShopVR integrates object hierarchy management while keeping the backpropagated gradient flow to allow local editing with context information. © 2025 IEEE.},
keywords = {3D authoring, 3D modeling, Digital replicas, Gaussian distribution, Gaussian Splatting editing, Gaussians, Graphical user interfaces, High quality, Immersive, Immersive environment, Interactive computer graphics, Rendering (computer graphics), Rendering pipelines, Splatting, Three dimensional computer graphics, User profile, Virtual Reality, Virtual reality user interface, Virtualization, VR user interface},
pubstate = {published},
tppubtype = {inproceedings}
}
Mao, H.; Xu, Z.; Wei, S.; Quan, Y.; Deng, N.; Yang, X.
LLM-powered Gaussian Splatting in VR interactions Proceedings Article
In: Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW, pp. 1654–1655, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833151484-6 (ISBN).
Abstract | Links | BibTeX | Tags: 3D Gaussian Splatting, 3D reconstruction, Content creation, Digital elevation model, Gaussians, High quality, Language Model, material analysis, Materials analysis, Physical simulation, Quality rendering, Rendering (computer graphics), Splatting, Virtual Reality, Volume Rendering, VR systems
@inproceedings{mao_llm-powered_2025,
title = {LLM-powered Gaussian Splatting in VR interactions},
author = {H. Mao and Z. Xu and S. Wei and Y. Quan and N. Deng and X. Yang},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005148017&doi=10.1109%2fVRW66409.2025.00472&partnerID=40&md5=ee725f655a37251ff335ad2098d15f22},
doi = {10.1109/VRW66409.2025.00472},
isbn = {979-833151484-6 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW},
pages = {1654–1655},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Recent advances in radiance field rendering, particularly 3D Gaussian Splatting (3DGS), have demonstrated significant potential for VR content creation, offering both high-quality rendering and an efficient production pipeline. However, current physics-based interaction systems for 3DGS are limited to either simplistic, unrealistic simulations or require substantial user input for complex scenes, largely due to the lack of scene comprehension. In this demonstration, we present a highly realistic interactive VR system powered by large language models (LLMs). After object-aware GS reconstruction, we prompt GPT-4o to analyze the physical properties of objects in the scene, which then guide physical simulations that adhere to real-world phenomena. Additionally, We design a GPT-assisted GS inpainting module to complete the areas occluded by manipulated objects. To facilitate rich interaction, we introduce a computationally efficient physical simulation framework through a PBD-based unified interpolation method, which supports various forms of physical interactions. In our research demonstrations, we reconstruct varieties of scenes enhanced by LLM's understanding, showcasing how our VR system can support complex, realistic interactions without additional manual design or annotation. © 2025 IEEE.},
keywords = {3D Gaussian Splatting, 3D reconstruction, Content creation, Digital elevation model, Gaussians, High quality, Language Model, material analysis, Materials analysis, Physical simulation, Quality rendering, Rendering (computer graphics), Splatting, Virtual Reality, Volume Rendering, VR systems},
pubstate = {published},
tppubtype = {inproceedings}
}
Vachha, C.; Kang, Y.; Dive, Z.; Chidambaram, A.; Gupta, A.; Jun, E.; Hartmann, B.
Dreamcrafter: Immersive Editing of 3D Radiance Fields Through Flexible, Generative Inputs and Outputs Proceedings Article
In: Conf Hum Fact Comput Syst Proc, Association for Computing Machinery, 2025, ISBN: 979-840071394-1 (ISBN).
Abstract | Links | BibTeX | Tags: 3D modeling, 3D scenes, AI assisted creativity tool, Animation, Computer vision, Direct manipulation, Drawing (graphics), Gaussian Splatting, Gaussians, Generative AI, Graphic, Graphics, High level languages, Immersive, Interactive computer graphics, Splatting, Three dimensional computer graphics, Virtual Reality, Worldbuilding interface
@inproceedings{vachha_dreamcrafter_2025,
title = {Dreamcrafter: Immersive Editing of 3D Radiance Fields Through Flexible, Generative Inputs and Outputs},
author = {C. Vachha and Y. Kang and Z. Dive and A. Chidambaram and A. Gupta and E. Jun and B. Hartmann},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005725679&doi=10.1145%2f3706598.3714312&partnerID=40&md5=68cf2a08d3057fd9756e25d53959872b},
doi = {10.1145/3706598.3714312},
isbn = {979-840071394-1 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Conf Hum Fact Comput Syst Proc},
publisher = {Association for Computing Machinery},
abstract = {Authoring 3D scenes is a central task for spatial computing applications. Competing visions for lowering existing barriers are (1) focus on immersive, direct manipulation of 3D content or (2) leverage AI techniques that capture real scenes (3D Radiance Fields such as, NeRFs, 3D Gaussian Splatting) and modify them at a higher level of abstraction, at the cost of high latency. We unify the complementary strengths of these approaches and investigate how to integrate generative AI advances into real-time, immersive 3D Radiance Field editing. We introduce Dreamcrafter, a VR-based 3D scene editing system that: (1) provides a modular architecture to integrate generative AI algorithms; (2) combines different levels of control for creating objects, including natural language and direct manipulation; and (3) introduces proxy representations that support interaction during high-latency operations. We contribute empirical findings on control preferences and discuss how generative AI interfaces beyond text input enhance creativity in scene editing and world building. © 2025 Copyright held by the owner/author(s).},
keywords = {3D modeling, 3D scenes, AI assisted creativity tool, Animation, Computer vision, Direct manipulation, Drawing (graphics), Gaussian Splatting, Gaussians, Generative AI, Graphic, Graphics, High level languages, Immersive, Interactive computer graphics, Splatting, Three dimensional computer graphics, Virtual Reality, Worldbuilding interface},
pubstate = {published},
tppubtype = {inproceedings}
}
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}
}
2024
Cronin, I.
Apress Media LLC, 2024, ISBN: 979-886880282-9 (ISBN); 979-886880281-2 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Augmented Reality, Autonomous system, Autonomous systems, Business applications, Computer vision, Decision making, Gaussian Splatting, Gaussians, Generative AI, Language processing, Learning algorithms, Learning systems, machine learning, Machine-learning, Natural Language Processing, Natural Language Processing (NLP), Natural language processing systems, Natural languages, Splatting
@book{cronin_understanding_2024,
title = {Understanding Generative AI Business Applications: A Guide to Technical Principles and Real-World Applications},
author = {I. Cronin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001777571&doi=10.1007%2f979-8-8688-0282-9&partnerID=40&md5=c0714ff3e1ad755596426ea092b830d6},
doi = {10.1007/979-8-8688-0282-9},
isbn = {979-886880282-9 (ISBN); 979-886880281-2 (ISBN)},
year = {2024},
date = {2024-01-01},
publisher = {Apress Media LLC},
series = {Understanding Generative AI Business Applications: A Guide to Technical Principles and Real-World Applications},
abstract = {This guide covers the fundamental technical principles and various business applications of Generative AI for planning, developing, and evaluating AI-driven products. It equips you with the knowledge you need to harness the potential of Generative AI for enhancing business creativity and productivity. The book is organized into three sections: text-based, senses-based, and rationale-based. Each section provides an in-depth exploration of the specific methods and applications of Generative AI. In the text-based section, you will find detailed discussions on designing algorithms to automate and enhance written communication, including insights into the technical aspects of transformer-based Natural Language Processing (NLP) and chatbot architecture, such as GPT-4, Claude 2, Google Bard, and others. The senses-based section offers a glimpse into the algorithms and data structures that underpin visual, auditory, and multisensory experiences, including NeRF, 3D Gaussian Splatting, Stable Diffusion, AR and VR technologies, and more. The rationale-based section illuminates the decision-making capabilities of AI, with a focus on machine learning and data analytics techniques that empower applications such as simulation models, agents, and autonomous systems. In summary, this book serves as a guide for those seeking to navigate the dynamic landscape of Generative AI. Whether you’re a seasoned AI professional or a business leader looking to harness the power of creative automation, these pages offer a roadmap to leverage Generative AI for your organization’s success. © 2024 by Irena Cronin.},
keywords = {Artificial intelligence, Augmented Reality, Autonomous system, Autonomous systems, Business applications, Computer vision, Decision making, Gaussian Splatting, Gaussians, Generative AI, Language processing, Learning algorithms, Learning systems, machine learning, Machine-learning, Natural Language Processing, Natural Language Processing (NLP), Natural language processing systems, Natural languages, Splatting},
pubstate = {published},
tppubtype = {book}
}