Abstract
This paper introduces a two-fold methodology of creating fabric designs and patterns, using both traditional object detection and Deep Learning methodologies. The proposed methodology first augments a given partial sketch, which is taken as an input from the user. This sketch augmentation is performed through a combination of object detection, canvas quilting, and seamless tiling, to achieve a repeatable block of a pattern. This augmented pattern is then carried forward as an input to our variation of the pix2pix GAN, which outputs a styled and colored pattern using the sketch as a baseline. This design pipeline is an overall overhaul of the creative process of a textile designer, and is intended to provide assistance in the design of modern textiles in the industry by reducing the time from going to a sketch to a pattern in under a minute.
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Ahmed, O., Abid, M.S., Junaid, A., Raza, S.S. (2023). Fabric Sketch Augmentation & Styling via Deep Learning & Image Synthesis. In: Johnson, C., Rodríguez-Fernández, N., Rebelo, S.M. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2023. Lecture Notes in Computer Science, vol 13988. Springer, Cham. https://doi.org/10.1007/978-3-031-29956-8_21
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