Publications

@article{kim_3d_2025,

title = {3D Digital Human Generation from a Single Image Using Generative AI with Real-Time Motion Synchronization},

author = {M. Kim and T. Kim and K. -T. Lee},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218855876&doi=10.3390%2felectronics14040777&partnerID=40&md5=f1d0a0238c6422327901e4d4b6a43727},

doi = {10.3390/electronics14040777},

issn = {20799292 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Electronics (Switzerland)},

volume = {14},

number = {4},

abstract = {The generation of 3D digital humans has traditionally relied on multi-view imaging systems and large-scale datasets, posing challenges in cost, accessibility, and real-time applicability. To overcome these limitations, this study presents an efficient pipeline that constructs high-fidelity 3D digital humans from a single frontal image. By leveraging generative AI, the system synthesizes additional views and generates UV maps compatible with the SMPL-X model, ensuring anatomically accurate and photorealistic reconstructions. The generated 3D models are imported into Unity 3D, where they are rigged for real-time motion synchronization using BlazePose-based lightweight pose estimation. To further enhance motion realism, custom algorithms—including ground detection and rotation smoothing—are applied, improving movement stability and fluidity. The system was rigorously evaluated through both quantitative and qualitative analyses. Results show an average generation time of 211.1 s, segmentation accuracy of 92.1%, and real-time rendering at 64.4 FPS. In qualitative assessments, expert reviewers rated the system using the SUS usability framework and heuristic evaluation, confirming its usability and effectiveness. This method eliminates the need for multi-view cameras or depth sensors, significantly reducing the barrier to entry for real-time 3D avatar creation and interactive AI-driven applications. It has broad applications in virtual reality (VR), gaming, digital content creation, AI-driven simulation, digital twins, and telepresence systems. By introducing a scalable and accessible 3D modeling pipeline, this research lays the groundwork for future advancements in immersive and interactive environments. © 2025 by the authors.},

keywords = {3D digital human, 3D human generation, digital twin, Generative AI, pose estimation, real-time motion synchronization, single image processing, SMPL-X, Unity 3D},

pubstate = {published},

tppubtype = {article}

}

@article{tang_metaverse_2025,

title = {Metaverse and Digital Twins in the Age of AI and Extended Reality},

author = {M. Tang and M. Nikolaenko and A. Alrefai and A. Kumar},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105008903949&doi=10.3390%2farchitecture5020036&partnerID=40&md5=3b05b81a0cf25d3c441d4701a7749d66},

doi = {10.3390/architecture5020036},

issn = {26738945 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Architecture},

volume = {5},

number = {2},

abstract = {This paper explores the evolving relationship between Digital Twins (DT) and the Metaverse, two foundational yet often conflated digital paradigms in digital architecture. While DTs function as mirrored models of real-world systems—integrating IoT, BIM, and real-time analytics to support decision-making—Metaverses are typically fictional, immersive, multi-user environments shaped by social, cultural, and speculative narratives. Through several research projects, the team investigate the divergence between DTs and Metaverses through the lens of their purpose, data structure, immersion, and interactivity, while highlighting areas of convergence driven by emerging technologies in Artificial Intelligence (AI) and Extended Reality (XR).This study aims to investigate the convergence of DTs and the Metaverse in digital architecture, examining how emerging technologies—such as AI, XR, and Large Language Models (LLMs)—are blurring their traditional boundaries. By analyzing their divergent purposes, data structures, and interactivity modes, as well as hybrid applications (e.g., data-integrated virtual environments and AI-driven collaboration), this study seeks to define the opportunities and challenges of this integration for architectural design, decision-making, and immersive user experiences. Our research spans multiple projects utilizing XR and AI to develop DT and the Metaverse. The team assess the capabilities of AI in DT environments, such as reality capture and smart building management. Concurrently, the team evaluates metaverse platforms for online collaboration and architectural education, focusing on features facilitating multi-user engagement. The paper presents evaluations of various virtual environment development pipelines, comparing traditional BIM+IoT workflows with novel approaches such as Gaussian Splatting and generative AI for content creation. The team further explores the integration of Large Language Models (LLMs) in both domains, such as virtual agents or LLM-powered Non-Player-Controlled Characters (NPC), enabling autonomous interaction and enhancing user engagement within spatial environments. Finally, the paper argues that DTs and Metaverse’s once-distinct boundaries are becoming increasingly porous. Hybrid digital spaces—such as virtual buildings with data-integrated twins and immersive, social metaverses—demonstrate this convergence. As digital environments mature, architects are uniquely positioned to shape these dual-purpose ecosystems, leveraging AI, XR, and spatial computing to fuse data-driven models with immersive and user-centered experiences. © 2025 by the authors.},

keywords = {AI, digital twin, Extended reality, Metaverse},

pubstate = {published},

tppubtype = {article}

}

@article{donekal_chandrashekar_understanding_2024,

title = {Understanding User Behavior for Enhancing Cybersecurity Training with Immersive Gamified Platforms},

author = {N. Donekal Chandrashekar and A. Lee and M. Azab and D. Gracanin},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213435167&doi=10.3390%2finfo15120814&partnerID=40&md5=134c43c7238bae4923468bc6e46c860d},

doi = {10.3390/info15120814},

issn = {20782489 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Information (Switzerland)},

volume = {15},

number = {12},

abstract = {In modern digital infrastructure, cyber systems are foundational, making resilience against sophisticated attacks essential. Traditional cybersecurity defenses primarily address technical vulnerabilities; however, the human element, particularly decision-making during cyber attacks, adds complexities that current behavioral studies fail to capture adequately. Existing approaches, including theoretical models, game theory, and simulators, rely on retrospective data and static scenarios. These methods often miss the real-time, context-specific nature of user responses during cyber threats. To address these limitations, this work introduces a framework that combines Extended Reality (XR) and Generative Artificial Intelligence (Gen-AI) within a gamified platform. This framework enables continuous, high-fidelity data collection on user behavior in dynamic attack scenarios. It includes three core modules: the Player Behavior Module (PBM), Gamification Module (GM), and Simulation Module (SM). Together, these modules create an immersive, responsive environment for studying user interactions. A case study in a simulated critical infrastructure environment demonstrates the framework’s effectiveness in capturing realistic user behaviors under cyber attack, with potential applications for improving response strategies and resilience across critical sectors. This work lays the foundation for adaptive cybersecurity training and user-centered development across critical infrastructure. © 2024 by the authors.},

keywords = {Artificial intelligence, Critical infrastructures, Cyber attacks, Cyber security, Cyber systems, Cyber-attacks, Cybersecurity, Decisions makings, Digital infrastructures, digital twin, Extended reality, Gamification, Immersive, Network Security, simulation, Technical vulnerabilities, Training, user behavior, User behaviors},

pubstate = {published},

tppubtype = {article}

}

@article{otoum_machine_2024,

title = {Machine Learning in Metaverse Security: Current Solutions and Future Challenges},

author = {Y. Otoum and N. Gottimukkala and N. Kumar and A. Nayak},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193466017&doi=10.1145%2f3654663&partnerID=40&md5=b35485c5f2e943ec105ea11a80712cbe},

doi = {10.1145/3654663},

issn = {03600300 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {ACM Computing Surveys},

volume = {56},

number = {8},

abstract = {The Metaverse, positioned as the next frontier of the Internet, has the ambition to forge a virtual shared realm characterized by immersion, hyper-spatiotemporal dynamics, and self-sustainability. Recent technological strides in AI, Extended Reality, 6G, and blockchain propel the Metaverse closer to realization, gradually transforming it from science fiction into an imminent reality. Nevertheless, the extensive deployment of the Metaverse faces substantial obstacles, primarily stemming from its potential to infringe on privacy and be susceptible to security breaches, whether inherent in its underlying technologies or arising from the evolving digital landscape. Metaverse security provisioning is poised to confront various foundational challenges owing to its distinctive attributes, encompassing immersive realism, hyper-spatiotemporally, sustainability, and heterogeneity. This article undertakes a comprehensive study of the security and privacy challenges facing the Metaverse, leveraging machine learning models for this purpose. In particular, our focus centers on an innovative distributed Metaverse architecture characterized by interactions across 3D worlds. Subsequently, we conduct a thorough review of the existing cutting-edge measures designed for Metaverse systems while also delving into the discourse surrounding security and privacy threats. As we contemplate the future of Metaverse systems, we outline directions for open research pursuits in this evolving landscape. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.},

keywords = {'current, Block-chain, Blockchain, digital twin, E-Learning, Extended reality, Future challenges, Generative AI, machine learning, Machine-learning, Metaverse Security, Metaverses, Security and privacy, Spatio-temporal dynamics, Sustainable development},

pubstate = {published},

tppubtype = {article}

}