AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2025
Zeng, S. -Y.; Liang, T. -Y.
PartConverter: A Part-Oriented Transformation Framework for Point Clouds Journal Article
In: IET Image Processing, vol. 19, no. 1, 2025, ISSN: 17519659 (ISSN); 17519667 (ISSN), (Publisher: John Wiley and Sons Inc).
Abstract | Links | BibTeX | Tags: 3D modeling, 3D models, 3d-modeling, Adversarial networks, attention mechanism, Attention mechanisms, Auto encoders, Cloud transformations, Generative Adversarial Network, Part assembler, Part-oriented, Point cloud transformation, Point-clouds
@article{zeng_partconverter_2025,
title = {PartConverter: A Part-Oriented Transformation Framework for Point Clouds},
author = {S. -Y. Zeng and T. -Y. Liang},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005775417&doi=10.1049%2Fipr2.70104&partnerID=40&md5=d1eccf7d6b58a93978c55e8f404be38b},
doi = {10.1049/ipr2.70104},
issn = {17519659 (ISSN); 17519667 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IET Image Processing},
volume = {19},
number = {1},
abstract = {With generative AI technologies advancing rapidly, the capabilities for 3D model generation and transformation are expanding across industries like manufacturing, healthcare, and virtual reality. However, existing methods based on generative adversarial networks (GANs), autoencoders, or transformers still have notable limitations. They primarily generate entire objects without providing flexibility for independent part transformation or precise control over model components. These constraints pose challenges for applications requiring complex object manipulation and fine-grained adjustments. To overcome these limitations, we propose PartConverter, a novel part-oriented point cloud transformation framework emphasizing flexibility and precision in 3D model transformations. PartConverter leverages attention mechanisms and autoencoders to capture crucial details within each part while modeling the relationships between components, thereby enabling highly customizable, part-wise transformations that maintain overall consistency. Additionally, our part assembler ensures that transformed parts align coherently, resulting in a consistent and realistic final 3D shape. This framework significantly enhances control over detailed part modeling, increasing the flexibility and efficiency of 3D model transformation workflows. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: John Wiley and Sons Inc},
keywords = {3D modeling, 3D models, 3d-modeling, Adversarial networks, attention mechanism, Attention mechanisms, Auto encoders, Cloud transformations, Generative Adversarial Network, Part assembler, Part-oriented, Point cloud transformation, Point-clouds},
pubstate = {published},
tppubtype = {article}
}
With generative AI technologies advancing rapidly, the capabilities for 3D model generation and transformation are expanding across industries like manufacturing, healthcare, and virtual reality. However, existing methods based on generative adversarial networks (GANs), autoencoders, or transformers still have notable limitations. They primarily generate entire objects without providing flexibility for independent part transformation or precise control over model components. These constraints pose challenges for applications requiring complex object manipulation and fine-grained adjustments. To overcome these limitations, we propose PartConverter, a novel part-oriented point cloud transformation framework emphasizing flexibility and precision in 3D model transformations. PartConverter leverages attention mechanisms and autoencoders to capture crucial details within each part while modeling the relationships between components, thereby enabling highly customizable, part-wise transformations that maintain overall consistency. Additionally, our part assembler ensures that transformed parts align coherently, resulting in a consistent and realistic final 3D shape. This framework significantly enhances control over detailed part modeling, increasing the flexibility and efficiency of 3D model transformation workflows. © 2025 Elsevier B.V., All rights reserved.
Jayanthy, S.; Selvaganesh, M.; Kumar, S. Sakthi; Sathish, A. Manjunatha; Sabarisan, K. M.; Arasi, T. Senthamil
Generative AI Solution for CNC Machines and Robotics Code Generation Proceedings Article
In: Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331536695 (ISBN).
Abstract | Links | BibTeX | Tags: Adaptive control systems, Adversarial networks, Automated Code Generation, Automatic programming, CNC machine, CNC Machines, CNC system, Codegeneration, Computer aided instruction, Computer control, Computer control systems, E-Learning, Edge computing, Federated learning, Flow control, GANs, Generative pre-trained transformer transformer, GPT Transformers, Industrial research, Industry 4.0, Innovative approaches, Intelligent robots, Learning algorithms, Personnel training, Reinforcement Learning, Reinforcement learnings, Robotic systems, Simulation platform, Smart manufacturing, Virtual Reality
@inproceedings{jayanthy_generative_2025,
title = {Generative AI Solution for CNC Machines and Robotics Code Generation},
author = {S. Jayanthy and M. Selvaganesh and S. Sakthi Kumar and A. Manjunatha Sathish and K. M. Sabarisan and T. Senthamil Arasi},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011963078&doi=10.1109%2FICCIES63851.2025.11033032&partnerID=40&md5=fb9143cd22dc48ae6c557f722cc2d6ab},
doi = {10.1109/ICCIES63851.2025.11033032},
isbn = {9798331536695 (ISBN)},
year = {2025},
date = {2025-01-01},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The advent of Industry 4.0 has revolutionized the manufacturing landscape, driving significant advancements in automation and intelligence. This study introduces an innovative approach to automated code generation for CNC and robotic systems, leveraging Generative Adversarial Networks (GANs) and GPT(Generative Pre-trained Transformer) Transformers. These AI models enable precise and optimized code creation, minimizing manual errors. Adaptive process control, achieved through Reinforcement Learning (RL), allows real-time adjustments to operational parameters, enhancing performance in dynamic environments. The incorporation of natural language processing through Transformer models facilitates intuitive operator interactions via user-friendly interfaces. Immersive Virtual Reality (VR) technologies provide high-fidelity simulation and training platforms for realistic testing and control. Additionally, collaborative learning mechanisms, achieved through Federated Learning and Edge-cloud computing, support continuous improvement and scalable deployment. Impressive outcomes were attained by the system, including 90.5% training efficiency, 98.7% coding accuracy, 95.2% adaptability, and 93.4% operator satisfaction. Experimental results validate the system's superior accuracy, adaptability, and user-centric design, showcasing its potential to revolutionize manufacturing processes. This research sets a new benchmark for intelligent, efficient, and scalable automation in the Industry 4.0 era, paving the way for transformative innovations in smart manufacturing. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Adaptive control systems, Adversarial networks, Automated Code Generation, Automatic programming, CNC machine, CNC Machines, CNC system, Codegeneration, Computer aided instruction, Computer control, Computer control systems, E-Learning, Edge computing, Federated learning, Flow control, GANs, Generative pre-trained transformer transformer, GPT Transformers, Industrial research, Industry 4.0, Innovative approaches, Intelligent robots, Learning algorithms, Personnel training, Reinforcement Learning, Reinforcement learnings, Robotic systems, Simulation platform, Smart manufacturing, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
The advent of Industry 4.0 has revolutionized the manufacturing landscape, driving significant advancements in automation and intelligence. This study introduces an innovative approach to automated code generation for CNC and robotic systems, leveraging Generative Adversarial Networks (GANs) and GPT(Generative Pre-trained Transformer) Transformers. These AI models enable precise and optimized code creation, minimizing manual errors. Adaptive process control, achieved through Reinforcement Learning (RL), allows real-time adjustments to operational parameters, enhancing performance in dynamic environments. The incorporation of natural language processing through Transformer models facilitates intuitive operator interactions via user-friendly interfaces. Immersive Virtual Reality (VR) technologies provide high-fidelity simulation and training platforms for realistic testing and control. Additionally, collaborative learning mechanisms, achieved through Federated Learning and Edge-cloud computing, support continuous improvement and scalable deployment. Impressive outcomes were attained by the system, including 90.5% training efficiency, 98.7% coding accuracy, 95.2% adaptability, and 93.4% operator satisfaction. Experimental results validate the system's superior accuracy, adaptability, and user-centric design, showcasing its potential to revolutionize manufacturing processes. This research sets a new benchmark for intelligent, efficient, and scalable automation in the Industry 4.0 era, paving the way for transformative innovations in smart manufacturing. © 2025 Elsevier B.V., All rights reserved.
Huang, D.; Ge, M.; Xiang, K.; Zhang, X.; Yang, H.
Privacy Preservation of Large Language Models in the Metaverse Era: Research Frontiers, Categorical Comparisons, and Future Directions Journal Article
In: International Journal of Network Management, vol. 35, no. 1, 2025, ISSN: 10557148 (ISSN); 10991190 (ISSN), (Publisher: John Wiley and Sons Ltd).
Abstract | Links | BibTeX | Tags: Adversarial networks, Computational Linguistics, Cryptography, Differential privacies, Excel, Language Model, Large language model, large language models, Life cycle, Metaverse, Metaverses, Natural language processing systems, Natural languages, Privacy preservation, Privacy protection, Research frontiers
@article{huang_privacy_2025,
title = {Privacy Preservation of Large Language Models in the Metaverse Era: Research Frontiers, Categorical Comparisons, and Future Directions},
author = {D. Huang and M. Ge and K. Xiang and X. Zhang and H. Yang},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199980257&doi=10.1002%2Fnem.2292&partnerID=40&md5=55662aeedfb216784f0ed398cf8bd2f0},
doi = {10.1002/nem.2292},
issn = {10557148 (ISSN); 10991190 (ISSN)},
year = {2025},
date = {2025-01-01},
booktitle = {Int J Network Manage},
journal = {International Journal of Network Management},
volume = {35},
number = {1},
publisher = {John Wiley and Sons Ltd},
abstract = {Large language models (LLMs), with their billions to trillions of parameters, excel in natural language processing, machine translation, dialog systems, and text summarization. These capabilities are increasingly pivotal in the metaverse, where they can enhance virtual interactions and environments. However, their extensive use, particularly in the metaverse's immersive platforms, raises significant privacy concerns. This paper analyzes existing privacy issues in LLMs, vital for both traditional and metaverse applications, and examines protection techniques across the entire life cycle of these models, from training to user deployment. We delve into cryptography, embedding layer encoding, differential privacy and its variants, and adversarial networks, highlighting their relevance in the metaverse context. Specifically, we explore technologies like homomorphic encryption and secure multiparty computation, which are essential for metaverse security. Our discussion on Gaussian differential privacy, Renyi differential privacy, Edgeworth accounting, and the generation of adversarial samples and loss functions emphasizes their importance in the metaverse's dynamic and interactive environments. Lastly, the paper discusses the current research status and future challenges in the security of LLMs within and beyond the metaverse, emphasizing urgent problems and potential areas for exploration. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: John Wiley and Sons Ltd},
keywords = {Adversarial networks, Computational Linguistics, Cryptography, Differential privacies, Excel, Language Model, Large language model, large language models, Life cycle, Metaverse, Metaverses, Natural language processing systems, Natural languages, Privacy preservation, Privacy protection, Research frontiers},
pubstate = {published},
tppubtype = {article}
}
Large language models (LLMs), with their billions to trillions of parameters, excel in natural language processing, machine translation, dialog systems, and text summarization. These capabilities are increasingly pivotal in the metaverse, where they can enhance virtual interactions and environments. However, their extensive use, particularly in the metaverse's immersive platforms, raises significant privacy concerns. This paper analyzes existing privacy issues in LLMs, vital for both traditional and metaverse applications, and examines protection techniques across the entire life cycle of these models, from training to user deployment. We delve into cryptography, embedding layer encoding, differential privacy and its variants, and adversarial networks, highlighting their relevance in the metaverse context. Specifically, we explore technologies like homomorphic encryption and secure multiparty computation, which are essential for metaverse security. Our discussion on Gaussian differential privacy, Renyi differential privacy, Edgeworth accounting, and the generation of adversarial samples and loss functions emphasizes their importance in the metaverse's dynamic and interactive environments. Lastly, the paper discusses the current research status and future challenges in the security of LLMs within and beyond the metaverse, emphasizing urgent problems and potential areas for exploration. © 2025 Elsevier B.V., All rights reserved.
Saddik, A. El; Ahmad, J.; Khan, M.; Abouzahir, S.; Gueaieb, W.
Unleashing Creativity in the Metaverse: Generative AI and Multimodal Content Journal Article
In: ACM Transactions on Multimedia Computing, Communications and Applications, vol. 21, no. 7, pp. 1–43, 2025, ISSN: 15516857 (ISSN); 15516865 (ISSN), (Publisher: Association for Computing Machinery).
Abstract | Links | BibTeX | Tags: Adversarial networks, Artificial intelligence, Content generation, Context information, Creatives, Diffusion Model, diffusion models, Generative adversarial networks, Generative AI, Human engineering, Information instructions, Interactive computer graphics, Interactive computer systems, Interactive devices, Interoperability, Metaverse, Metaverses, Multi-modal, multimodal, Simple++, Three dimensional computer graphics, user experience, User interfaces, Virtual Reality
@article{el_saddik_unleashing_2025,
title = {Unleashing Creativity in the Metaverse: Generative AI and Multimodal Content},
author = {A. El Saddik and J. Ahmad and M. Khan and S. Abouzahir and W. Gueaieb},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011860002&doi=10.1145%2F3713075&partnerID=40&md5=20064843ced240c42e9353d747672cb3},
doi = {10.1145/3713075},
issn = {15516857 (ISSN); 15516865 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {ACM Transactions on Multimedia Computing, Communications and Applications},
volume = {21},
number = {7},
pages = {1–43},
abstract = {The metaverse presents an emerging creative expression and collaboration frontier where generative artificial intelligence (GenAI) can play a pivotal role with its ability to generate multimodal content from simple prompts. These prompts allow the metaverse to interact with GenAI, where context information, instructions, input data, or even output indications constituting the prompt can come from within the metaverse. However, their integration poses challenges regarding interoperability, lack of standards, scalability, and maintaining a high-quality user experience. This article explores how GenAI can productively assist in enhancing creativity within the contexts of the metaverse and unlock new opportunities. We provide a technical, in-depth overview of the different generative models for image, video, audio, and 3D content within the metaverse environments. We also explore the bottlenecks, opportunities, and innovative applications of GenAI from the perspectives of end users, developers, service providers, and AI researchers. This survey commences by highlighting the potential of GenAI for enhancing the metaverse experience through dynamic content generation to populate massive virtual worlds. Subsequently, we shed light on the ongoing research practices and trends in multimodal content generation, enhancing realism and creativity and alleviating bottlenecks related to standardization, computational cost, privacy, and safety. Last, we share insights into promising research directions toward the integration of GenAI with the metaverse for creative enhancement, improved immersion, and innovative interactive applications. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Association for Computing Machinery},
keywords = {Adversarial networks, Artificial intelligence, Content generation, Context information, Creatives, Diffusion Model, diffusion models, Generative adversarial networks, Generative AI, Human engineering, Information instructions, Interactive computer graphics, Interactive computer systems, Interactive devices, Interoperability, Metaverse, Metaverses, Multi-modal, multimodal, Simple++, Three dimensional computer graphics, user experience, User interfaces, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
The metaverse presents an emerging creative expression and collaboration frontier where generative artificial intelligence (GenAI) can play a pivotal role with its ability to generate multimodal content from simple prompts. These prompts allow the metaverse to interact with GenAI, where context information, instructions, input data, or even output indications constituting the prompt can come from within the metaverse. However, their integration poses challenges regarding interoperability, lack of standards, scalability, and maintaining a high-quality user experience. This article explores how GenAI can productively assist in enhancing creativity within the contexts of the metaverse and unlock new opportunities. We provide a technical, in-depth overview of the different generative models for image, video, audio, and 3D content within the metaverse environments. We also explore the bottlenecks, opportunities, and innovative applications of GenAI from the perspectives of end users, developers, service providers, and AI researchers. This survey commences by highlighting the potential of GenAI for enhancing the metaverse experience through dynamic content generation to populate massive virtual worlds. Subsequently, we shed light on the ongoing research practices and trends in multimodal content generation, enhancing realism and creativity and alleviating bottlenecks related to standardization, computational cost, privacy, and safety. Last, we share insights into promising research directions toward the integration of GenAI with the metaverse for creative enhancement, improved immersion, and innovative interactive applications. © 2025 Elsevier B.V., All rights reserved.
Kammari, K. S.; Annambhotla, Y. L.; Khanna, M.
ProWGAN a hybrid generative adversarial network for automated landscape generation in media and video games Journal Article
In: Discover Artificial Intelligence, vol. 5, no. 1, 2025, ISSN: 27310809 (ISSN), (Publisher: Springer Nature).
Abstract | Links | BibTeX | Tags: 'current, 3D modeling, Adversarial networks, Generative AI, Hybrid model, Image production, Interactive computer graphics, Landscape, Landscapes, Motion pictures, Progressive GAN, Video-games, Videogame environment, Videogames environments, Virtual Reality, Wasserstein GAN
@article{kammari_prowgan_2025,
title = {ProWGAN a hybrid generative adversarial network for automated landscape generation in media and video games},
author = {K. S. Kammari and Y. L. Annambhotla and M. Khanna},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105017739103&doi=10.1007%2Fs44163-025-00512-5&partnerID=40&md5=5e48fe6941113d9196abb4308cf5db0f},
doi = {10.1007/s44163-025-00512-5},
issn = {27310809 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Discover Artificial Intelligence},
volume = {5},
number = {1},
abstract = {The current approaches of creating realistic, high-quality landscape imagery mostly depend on labor-intensive manual design procedures. In an effort to simplify image production for video games, virtual reality, and motion pictures, a new hybrid model called ProWGAN, combining ProGAN and WGAN approaches, is employed for automated landscape synthesis. Five models (FCGAN, DCGAN, ProGAN, WGAN, and ProWGAN) were trained on a dataset of landscape images and compared using multiple evaluation metrics. Compared to traditional models, ProWGAN produces 128-128 size images with the best FID score (29.67), IS (5.11), and lowest critic loss (0.2), fully capturing landscape features in just 5 h of training and 50 epochs. The layered method to producing images and progressive learning of ProGAN with the stability of WGAN’s Wasserstein distance showed superior ability to generate realistic landscape images. The results demonstrate how ProWGAN can revolutionize landscape image production by reducing manual work, lowering production time and effort and how a 2d image can be converted into 3d model via MeshRoom. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Springer Nature},
keywords = {'current, 3D modeling, Adversarial networks, Generative AI, Hybrid model, Image production, Interactive computer graphics, Landscape, Landscapes, Motion pictures, Progressive GAN, Video-games, Videogame environment, Videogames environments, Virtual Reality, Wasserstein GAN},
pubstate = {published},
tppubtype = {article}
}
The current approaches of creating realistic, high-quality landscape imagery mostly depend on labor-intensive manual design procedures. In an effort to simplify image production for video games, virtual reality, and motion pictures, a new hybrid model called ProWGAN, combining ProGAN and WGAN approaches, is employed for automated landscape synthesis. Five models (FCGAN, DCGAN, ProGAN, WGAN, and ProWGAN) were trained on a dataset of landscape images and compared using multiple evaluation metrics. Compared to traditional models, ProWGAN produces 128-128 size images with the best FID score (29.67), IS (5.11), and lowest critic loss (0.2), fully capturing landscape features in just 5 h of training and 50 epochs. The layered method to producing images and progressive learning of ProGAN with the stability of WGAN’s Wasserstein distance showed superior ability to generate realistic landscape images. The results demonstrate how ProWGAN can revolutionize landscape image production by reducing manual work, lowering production time and effort and how a 2d image can be converted into 3d model via MeshRoom. © 2025 Elsevier B.V., All rights reserved.
2024
Jayaraman, S.; Bhavya, R.; Srihari, V.; Rajam, V. Mary Anita
TexAVi: Generating Stereoscopic VR Video Clips from Text Descriptions Proceedings Article
In: IEEE Int. Conf. Comput. Vis. Mach. Intell., CVMI, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 9798350376876 (ISBN).
Abstract | Links | BibTeX | Tags: Adversarial networks, Computer simulation languages, Deep learning, Depth Estimation, Depth perception, Diffusion Model, diffusion models, Digital elevation model, Generative adversarial networks, Generative model, Generative systems, Language Model, Motion capture, Stereo image processing, Text-to-image, Training data, Video analysis, Video-clips, Virtual environments, Virtual Reality
@inproceedings{jayaraman_texavi_2024,
title = {TexAVi: Generating Stereoscopic VR Video Clips from Text Descriptions},
author = {S. Jayaraman and R. Bhavya and V. Srihari and V. Mary Anita Rajam},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215265234&doi=10.1109%2FCVMI61877.2024.10782691&partnerID=40&md5=21e6ecfcc0710c036ba93e39b5fcd30d},
doi = {10.1109/CVMI61877.2024.10782691},
isbn = {9798350376876 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {IEEE Int. Conf. Comput. Vis. Mach. Intell., CVMI},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {While generative models such as text-to-image, large language models and text-to-video have seen significant progress, the extension to text-to-virtual-reality remains largely unexplored, due to a deficit in training data and the complexity of achieving realistic depth and motion in virtual environments. This paper proposes an approach to coalesce existing generative systems to form a stereoscopic virtual reality video from text. Carried out in three main stages, we start with a base text-to-image model that captures context from an input text. We then employ Stable Diffusion on the rudimentary image produced, to generate frames with enhanced realism and overall quality. These frames are processed with depth estimation algorithms to create left-eye and right-eye views, which are stitched side-by-side to create an immersive viewing experience. Such systems would be highly beneficial in virtual reality production, since filming and scene building often require extensive hours of work and post-production effort. We utilize image evaluation techniques, specifically Fréchet Inception Distance and CLIP Score, to assess the visual quality of frames produced for the video. These quantitative measures establish the proficiency of the proposed method. Our work highlights the exciting possibilities of using natural language-driven graphics in fields like virtual reality simulations. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Adversarial networks, Computer simulation languages, Deep learning, Depth Estimation, Depth perception, Diffusion Model, diffusion models, Digital elevation model, Generative adversarial networks, Generative model, Generative systems, Language Model, Motion capture, Stereo image processing, Text-to-image, Training data, Video analysis, Video-clips, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
While generative models such as text-to-image, large language models and text-to-video have seen significant progress, the extension to text-to-virtual-reality remains largely unexplored, due to a deficit in training data and the complexity of achieving realistic depth and motion in virtual environments. This paper proposes an approach to coalesce existing generative systems to form a stereoscopic virtual reality video from text. Carried out in three main stages, we start with a base text-to-image model that captures context from an input text. We then employ Stable Diffusion on the rudimentary image produced, to generate frames with enhanced realism and overall quality. These frames are processed with depth estimation algorithms to create left-eye and right-eye views, which are stitched side-by-side to create an immersive viewing experience. Such systems would be highly beneficial in virtual reality production, since filming and scene building often require extensive hours of work and post-production effort. We utilize image evaluation techniques, specifically Fréchet Inception Distance and CLIP Score, to assess the visual quality of frames produced for the video. These quantitative measures establish the proficiency of the proposed method. Our work highlights the exciting possibilities of using natural language-driven graphics in fields like virtual reality simulations. © 2025 Elsevier B.V., All rights reserved.
Rosati, R.; Senesi, P.; Lonzi, B.; Mancini, A.; Mandolini, M.
An automated CAD-to-XR framework based on generative AI and Shrinkwrap modelling for a User-Centred design approach Journal Article
In: Advanced Engineering Informatics, vol. 62, 2024, ISSN: 14740346 (ISSN), (Publisher: Elsevier Ltd).
Abstract | Links | BibTeX | Tags: Adversarial networks, Artificial intelligence, CAD-to-XR, Computer aided design models, Computer aided logic design, Computer-aided design, Computer-aided design-to-XR, Design simplification, Digital elevation model, Digital storage, Extended reality, Flow visualization, Generative adversarial networks, Guns (armament), Helmet mounted displays, Intellectual property core, Mixed reality, Photo-realistic, Shrinkfitting, Structural dynamics, User centered design, User-centered design, User-centered design approaches, User-centred, Virtual Prototyping, Work-flows
@article{rosati_automated_2024,
title = {An automated CAD-to-XR framework based on generative AI and Shrinkwrap modelling for a User-Centred design approach},
author = {R. Rosati and P. Senesi and B. Lonzi and A. Mancini and M. Mandolini},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204897460&doi=10.1016%2Fj.aei.2024.102848&partnerID=40&md5=187d6f33f7232caac83c114fd5e03dea},
doi = {10.1016/j.aei.2024.102848},
issn = {14740346 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Advanced Engineering Informatics},
volume = {62},
abstract = {CAD-to-XR is the workflow to generate interactive Photorealistic Virtual Prototypes (iPVPs) for Extended Reality (XR) apps from Computer-Aided Design (CAD) models. This process entails modelling, texturing, and XR programming. In the literature, no automatic CAD-to-XR frameworks simultaneously manage CAD simplification and texturing. There are no examples of their adoption for User-Centered Design (UCD). Moreover, such CAD-to-XR workflows do not seize the potentialities of generative algorithms to produce synthetic images (textures). The paper presents a framework for implementing the CAD-to-XR workflow. The solution consists of a module for texture generation based on Generative Adversarial Networks (GANs). The generated texture is then managed by another module (based on Shrinkwrap modelling) to develop the iPVP by simplifying the 3D model and UV mapping the generated texture. The geometric and material data is integrated into a graphic engine, which allows for programming an interactive experience with the iPVP in XR. The CAD-to-XR framework was validated on two components (rifle stock and forend) of a sporting rifle. The solution can automate the texturing process of different product versions in shorter times (compared to a manual procedure). After each product revision, it avoids tedious and manual activities required to generate a new iPVP. The image quality metrics highlight that images are generated in a “realistic” manner (the perceived quality of generated textures is highly comparable to real images). The quality of the iPVPs, generated through the proposed framework and visualised by users through a mixed reality head-mounted display, is equivalent to traditionally designed prototypes. © 2024 Elsevier B.V., All rights reserved.},
note = {Publisher: Elsevier Ltd},
keywords = {Adversarial networks, Artificial intelligence, CAD-to-XR, Computer aided design models, Computer aided logic design, Computer-aided design, Computer-aided design-to-XR, Design simplification, Digital elevation model, Digital storage, Extended reality, Flow visualization, Generative adversarial networks, Guns (armament), Helmet mounted displays, Intellectual property core, Mixed reality, Photo-realistic, Shrinkfitting, Structural dynamics, User centered design, User-centered design, User-centered design approaches, User-centred, Virtual Prototyping, Work-flows},
pubstate = {published},
tppubtype = {article}
}
CAD-to-XR is the workflow to generate interactive Photorealistic Virtual Prototypes (iPVPs) for Extended Reality (XR) apps from Computer-Aided Design (CAD) models. This process entails modelling, texturing, and XR programming. In the literature, no automatic CAD-to-XR frameworks simultaneously manage CAD simplification and texturing. There are no examples of their adoption for User-Centered Design (UCD). Moreover, such CAD-to-XR workflows do not seize the potentialities of generative algorithms to produce synthetic images (textures). The paper presents a framework for implementing the CAD-to-XR workflow. The solution consists of a module for texture generation based on Generative Adversarial Networks (GANs). The generated texture is then managed by another module (based on Shrinkwrap modelling) to develop the iPVP by simplifying the 3D model and UV mapping the generated texture. The geometric and material data is integrated into a graphic engine, which allows for programming an interactive experience with the iPVP in XR. The CAD-to-XR framework was validated on two components (rifle stock and forend) of a sporting rifle. The solution can automate the texturing process of different product versions in shorter times (compared to a manual procedure). After each product revision, it avoids tedious and manual activities required to generate a new iPVP. The image quality metrics highlight that images are generated in a “realistic” manner (the perceived quality of generated textures is highly comparable to real images). The quality of the iPVPs, generated through the proposed framework and visualised by users through a mixed reality head-mounted display, is equivalent to traditionally designed prototypes. © 2024 Elsevier B.V., All rights reserved.