Generative modelling of semantic segmentation data in the fashion domain

In this work, we propose a method to generatively model the joint distribution of images and corresponding semantic segmentation maps using generative adversarial networks. We extend the Style-GAN architecture by iteratively growing the network during training, to add new output channels that model the semantic segmentation maps. We train the proposed method on a large dataset of fashion images and our experimental evaluation shows that the model produces samples that are coherent and plausible with semantic segmentation maps that closely match the semantics in the image.

Marie Korneliusson, John Martinsson, Olof Mogren

Second Workshop on Computer Vision for Fashion, Art and Design at ICCV
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