Disentangling Multiple Conditional Inputs in GANs

Gökhan Yildirim; Calvin Seward; Urs Bergmann

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arXiv · 1806.07819

Disentangling Multiple Conditional Inputs in GANs

Gökhan Yildirim·Calvin Seward·Urs Bergmann

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Abstract

In this paper, we propose a method that disentangles the effects of multiple input conditions in Generative Adversarial Networks (GANs). In particular, we demonstrate our method in controlling color, texture, and shape of a generated garment image for computer-aided fashion design. To disentangle the effect of input attributes, we customize conditional GANs with consistency loss functions. In our experiments, we tune one input at a time and show that we can guide our network to generate novel and realistic images of clothing articles. In addition, we present a fashion design process that estimates the input attributes of an existing garment and modifies them using our generator.

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Gökhan Yildirim, Calvin Seward, Urs Bergmann. 2018-06-20. Disentangling Multiple Conditional Inputs in GANs. https://arxiv.org/abs/1806.07819

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