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Paint by Word

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arxiv 2103.10951 v3 pith:OMYFGIEY submitted 2021-03-19 cs.CV cs.AIcs.GR

Alex Andonian , Sabrina Osmany , Audrey Cui , YeonHwan Park , Ali Jahanian , Antonio Torralba , David Bau This is my paper
classification cs.CVcs.AIcs.GR
keywords semanticimagechangesimportantmethodspaintpaintingable
verification ladder T0 review T1 audit T2 compute T3 formal
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We investigate the problem of zero-shot semantic image painting. Instead of painting modifications into an image using only concrete colors or a finite set of semantic concepts, we ask how to create semantic paint based on open full-text descriptions: our goal is to be able to point to a location in a synthesized image and apply an arbitrary new concept such as "rustic" or "opulent" or "happy dog." To do this, our method combines a state-of-the art generative model of realistic images with a state-of-the-art text-image semantic similarity network. We find that, to make large changes, it is important to use non-gradient methods to explore latent space, and it is important to relax the computations of the GAN to target changes to a specific region. We conduct user studies to compare our methods to several baselines.

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Cited by 5 Pith papers

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