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@article{200931,
        author = {Varsha Gurulingadevarmath and Goutham N},
        title = {Artificial Intelligence–Driven Image-to-Garment Translation Using CLO3D: Bridging Generative Design and Physical Production},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {2360-2364},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200931},
        abstract = {Generative artificial intelligence has transformed fashion concept development by producing detailed garment imagery from prompts or reference inputs. However, these images remain visually suggestive rather than technically complete, as they do not define seam structure, pattern geometry, fabric behavior, or construction sequence. This study addresses that limitation through an image-to-garment workflow that combines AI-generated imagery, CLO3D pattern reconstruction, fabric simulation, and manual construction. Using a case-study approach, the research traces the translation of a generated fashion image into a digital pattern set and then into a physical garment. The analysis shows that visual realism alone does not ensure manufacturability; successful translation depends on human interpretation, pattern adjustment, and simulation-based testing. CLO3D serves as the key bridge between conceptual imagery and physical production by exposing structural and material constraints that the image conceals. The findings suggest that AI can support early design exploration effectively, but technical pattern knowledge and manual production remain essential for turning generated visuals into wearable garments.},
        keywords = {Artificial intelligence, CLO3D, digital pattern making, garment construction, image-to-garment translation, fashion simulation},
        month = {May},
        }