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We propose a new task, called Story Visualization. Given a multi-sentenceparagraph, the story is visualized by generating a sequence of images, one foreach sentence. In contrast to video generation, story visualization focusesless on the continuity in generated images (frames), but more on the globalconsistency across dynamic scenes and characters -- a challenge that has notbeen addressed by any single-image or video generation methods. We thereforepropose a new story-to-image-sequence generation model, StoryGAN, based on thesequential conditional GAN framework. Our model is unique in that it consistsof a deep Context Encoder that dynamically tracks the story flow, and twodiscriminators at the story and image levels, to enhance the image quality andthe consistency of the generated sequences. To evaluate the model, we modifiedexisting datasets to create the CLEVR-SV and Pororo-SV datasets. Empirically,StoryGAN outperforms state-of-the-art models in image quality, contextualconsistency metrics, and human evaluation.

StoryGAN: A Sequential Conditional GAN for Story Visualization