Abstract
In this paper, we demonstrate automation of fashion assortment generation that appeals widely to consumer tastes given context in terms of attributes. We show how we trained generative adversarial networks to automatically generate an assortment given a fashion category (such as dresses and tops etc.) and its context (neck type, shape, color etc.), and describe the practical challenges we faced in terms of increasing assortment diversity. We explore different GAN architectures in context based fashion generation. We show that by providing context better quality images can be generated. Examples of taxonomy of design given a fashion article and finally automate generation of new designs that span the created taxonomy is shown. We also show a designer-in-loop process of taking a generated image to production level design templates (tech-packs). Here the designers bring their own creativity by adding elements, suggestive from the generated image, to accentuate the overall aesthetics of the final design.
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Banerjee, R.H., Rajagopal, A., Jha, N., Patro, A., Rajan, A. (2019). Let AI Clothe You: Diversified Fashion Generation. In: Carneiro, G., You, S. (eds) Computer Vision – ACCV 2018 Workshops. ACCV 2018. Lecture Notes in Computer Science(), vol 11367. Springer, Cham. https://doi.org/10.1007/978-3-030-21074-8_7
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DOI: https://doi.org/10.1007/978-3-030-21074-8_7
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