Publications

@inproceedings{lee_building_2024,

title = {Building Knowledge Base of 3D Object Assets Using Multimodal LLM AI Model},

author = {S. Lee and W. Park and K. Lee},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217636269&doi=10.1109%2fICTC62082.2024.10827434&partnerID=40&md5=581ee8ca50eb3dae15dc9675971cf428},

doi = {10.1109/ICTC62082.2024.10827434},

isbn = {21621233 (ISSN); 979-835036463-7 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Int. Conf. ICT Convergence},

pages = {416–418},

publisher = {IEEE Computer Society},

abstract = {The proliferation of various XR (eXtended Reality) services and the increasing incorporation of visual effects into existing content services have led to an exponential rise in the demand for 3D object assets. This paper describes an LLM (Large Language Model)-based multimodal AI model pipeline that can be applied to a generative AI model for creating new 3D objects or restructuring the asset management system to enhance the reusability of existing 3D objects. By leveraging a multimodal AI model, we derived descriptive text for assets such as 3D object, 2D image at a human-perceptible level, rather than mere data, and subsequently used an LLM to generate knowledge triplets for constructing an asset knowledge base. The applicability of this pipeline was verified using actual 3D objects from a content production company. Future work will focus on improving the quality of the generated knowledge triplets themselves by training the multimodal AI model with real-world content usage assets. © 2024 IEEE.},

keywords = {3D object, Asset management, Content services, Exponentials, Information Management, Knowledge Base, Language Model, Large language model, LLM, Multi-modal, Multi-Modal AI, Reusability, Visual effects, XR},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lee_knowledge_2023,

title = {Knowledge Generation Pipeline using LLM for Building 3D Object Knowledge Base},

author = {S. Lee and H. Lee and K. Lee},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184593202&doi=10.1109%2fICTC58733.2023.10392933&partnerID=40&md5=b877638607a04e5a31a2d5723af6e11b},

doi = {10.1109/ICTC58733.2023.10392933},

isbn = {21621233 (ISSN); 979-835031327-7 (ISBN)},

year  = {2023},

date = {2023-01-01},

booktitle = {Int. Conf. ICT Convergence},

pages = {1303–1305},

publisher = {IEEE Computer Society},

abstract = {With the wide spread of XR(eXtended Reality) contents such as Metaverse and VR(Virtual Reality) / AR(Augmented Reality), the utilization and importance of 3D objects are increasing. In this paper, we describe a knowledge generation pipeline of 3D object for reuse of existing 3D objects and production of new 3D object using generative AI(Artificial Intelligence). 3D object knowledge includes not only the object itself data that are generated in object editing phase but the information for human to recognize and understand objects. The target 3D model for building knowledge is the space model of office for business Metaverse service and the model of objects composing the space. LLM(Large Language Model)-based multimodal AI was used to extract knowledge from 3D model in a systematic and automated way. We plan to expand the pipeline to utilize knowledge base for managing extracted knowledge and correcting errors occurred during the LLM process for the knowledge extraction. © 2023 IEEE.},

keywords = {3D modeling, 3D models, 3D object, 3d-modeling, Augmented Reality, Data Mining, Knowledge Base, Knowledge based systems, Knowledge generations, Language Model, Metaverse, Metaverses, Multi-modal, MultiModal AI, Multimodal artificial intelligence, Pipelines, Virtual Reality, XR},

pubstate = {published},

tppubtype = {inproceedings}

}