3D Object Extraction Mechanism from Informal Natural Language Based Requirement Specifications 


Vol. 13,  No. 9, pp. 453-459, Sep.  2024
https://doi.org/10.3745/TKIPS.2024.13.9.453


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  Abstract

Recent advances in generative AI technologies using natural language processing have critically impacted text, image, and video production. Despite these innovations, we still need to improve the consistency and reusability of AI-generated outputs. These issues are critical in cartoon creation, where the inability to consistently replicate characters and specific objects can degrade the work's quality. We propose an integrated adaption of language analysis-based requirement engineering and cartoon engineering to solve this. The proposed method applies the linguistic frameworks of Chomsky and Fillmore to analyze natural language and utilizes UML sequence models for generating consistent 3D representations of object interactions. It systematically interprets the creator's intentions from textual inputs, ensuring that each character or object, once conceptualized, is accurately replicated across various panels and episodes to preserve visual and contextual integrity. This technique enhances the accuracy and consistency of character portrayals in animated contexts, aligning closely with the initial specifications. Consequently, this method holds potential applicability in other domains requiring the translation of complex textual descriptions into visual representations.

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[IEEE Style]

H. Kim, J. Kim, J. Kong, K. Kim, R. Y. C. Kim, "3D Object Extraction Mechanism from Informal Natural Language Based Requirement Specifications," The Transactions of the Korea Information Processing Society, vol. 13, no. 9, pp. 453-459, 2024. DOI: https://doi.org/10.3745/TKIPS.2024.13.9.453.

[ACM Style]

Hyuntae Kim, Janghwan Kim, Jihoon Kong, Kidu Kim, and R. Young Chul Kim. 2024. 3D Object Extraction Mechanism from Informal Natural Language Based Requirement Specifications. The Transactions of the Korea Information Processing Society, 13, 9, (2024), 453-459. DOI: https://doi.org/10.3745/TKIPS.2024.13.9.453.

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