Generative Models for Fashion Industry using Deep Neural Networks

Ildar Lomov; I. Makarov

doi:10.1109/CAIS.2019.8769486

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Generative Models for Fashion Industry using Deep Neural Networks

P. 1–6.

Ildar Lomov, Макаров И. А.

The progress of deep learning models in image and video processing leads to new artificial intelligence applications in Fashion industry. We consider the application of Generative Adversarial Networks and Neural Style Transfer for Digital Fashion presented as Virtual fashion for trying new clothes. Our model generate humans in clothes with respect to different fashion preferences, color layouts and fashion style. We propose that the virtual fashion industry will be highly impacted by accuracy of generating personalized human model taking into account different aspects of product and human preferences. We compare our model with state-of-art VITON model and show that using new perceptual loss in deep neural network architecture lead to better qualitative results in generating humans in clothes.

Язык: английский

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Ключевые слова: image generation generative adversarial networks Neural Style Transfer Computational Design Artificial Intelligence for Fashion Design Methodology Human Model Generation генеративные состязательные модели

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Proceedings of 2nd International Conference on Computer Applications & Information Security (ICCAIS)

NY: IEEE, 2019.

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