Generating Image Sequence from Description with LSTM Conditional GAN

Xu Ouyang; Xi Zhang; Di Ma; Gady Agam

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arXiv · 1806.03027

Generating Image Sequence from Description with LSTM Conditional GAN

Xu Ouyang·Xi Zhang·Di Ma·Gady Agam

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Abstract

Generating images from word descriptions is a challenging task. Generative adversarial networks(GANs) are shown to be able to generate realistic images of real-life objects. In this paper, we propose a new neural network architecture of LSTM Conditional Generative Adversarial Networks to generate images of real-life objects. Our proposed model is trained on the Oxford-102 Flowers and Caltech-UCSD Birds-200-2011 datasets. We demonstrate that our proposed model produces the better results surpassing other state-of-art approaches.

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BibTeX RIS

Xu Ouyang, Xi Zhang, Di Ma, Gady Agam. 2018-06-08. Generating Image Sequence from Description with LSTM Conditional GAN. https://arxiv.org/abs/1806.03027

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