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arXiv · 1512.00570

Attribute2Image: Conditional Image Generation from Visual Attributes

Xinchen Yan·Jimei Yang·Kihyuk Sohn·Honglak Lee
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Abstract

This paper investigates a novel problem of generating images from visual attributes. We model the image as a composite of foreground and background and develop a layered generative model with disentangled latent variables that can be learned end-to-end using a variational auto-encoder. We experiment with natural images of faces and birds and demonstrate that the proposed models are capable of generating realistic and diverse samples with disentangled latent representations. We use a general energy minimization algorithm for posterior inference of latent variables given novel images. Therefore, the learned generative models show excellent quantitative and visual results in the tasks of attribute-conditioned image reconstruction and completion.

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Xinchen Yan, Jimei Yang, Kihyuk Sohn, Honglak Lee. 2015-12-02. Attribute2Image: Conditional Image Generation from Visual Attributes. https://arxiv.org/abs/1512.00570

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